Final Project Draft

Introduction

The purpose of this project is to predict the final rankings in the 2024 World Figure Skating Championships for the Men’s and Women’s Single categories. Figure skating is an Olympic sport where the athlete performs individually, in pairs, or in groups on ice on figure skates. The Olympic disciplines are men’s single, women’s singles, pair skating, and ice dance, and those are also split within various levels from beginner to senior elite. For this project, we will focus on the senior elite level for men’s and women’s singles, since they will compete at the World Championships and have the same scoring styles, unlike pair skating and ice dance.

Figure skating competitions at the elite level are held over 2 days where skaters are expected to perform 2 programs, the short program and the free skate (also referred to as the free program), and earn scores for both programs. Their final score is based on the sum of the 2 scores.

Yuzuru Hanyu - Men’s Figure Skating

Why?

Typically, fans of the sport might predict the rankings of the World Championships based on a skater’s top score or average score of the current season. However, the problem with that is that each event has different sets of judges who may have different consistencies in scoring. A perfect skate at one event can be judged to be full of mistakes at another. Presentation and artistry are also evaluated, which could be subjective. The location of the event could also have an effect on the performance of a skater, like higher elevation could cause poorer performances. Therefore, the average scores of a competition could vary a lot and a winning score at one event could receive a lower score at another.

So, I wanted to see how the variation between competition scores could skew our ideas of the scores during the World Championships!

Data Description

All scores from International Skating Union recognized competitions are posted on their website. However it can be messy and have varying formats, so I used SkatingScores, which has the scores in clean, organized tables.

Within a regular figure skating season, there are 9 major ISU events leading up to Worlds: 6 Grand Prix events, a Grand Prix Final where the top 6 skaters during the 6 GP events get to compete, the European Championships, and the 4 Continents Championships. During a Winter Olympic year, the Olympics serve as an extra competition.

No skater will compete at all 9 events. 4 Continents is a competition for nations not part of Europe, so no skater will be competing in both the 4 Continents and European Championships. Grand Prix events are by invite only and skaters can only attend 2 at most. At most, a skater can attend 4 of these competitions or 5 for an Olympic year. At the least but rarely, a skater could not compete at any. So there could be potential missing data there, which we will look into.

I am using data from the 2016-2017, 2017-18, 2018-19, 2021-22, and 2022-23 competitive seasons. I skipped 2019-20 because the World Championships were cancelled due to COVID-19. I also skipped 2020-21 because a lot of events were cancelled also due to COVID, giving me insufficient data. The 2021-22 also did not have a Grand Prix Final event because of COVID, but otherwise there is a sufficient amount of data from that season.

Here is the regular score dataset, where I merged all of the scores from the 9/10 regular competitions over the mentioned seasons. I also added columns specifying the sex of the skater and the competition and the year of the score. I selected the year based on what year the corresponding World Championship competition occurs.

#merge regular scores
reg_merged <- dplyr::bind_rows(list(score23, score22, score19, score18, score17), .id = 'year')

#add year column
reg_merged <- reg_merged %>% 
  clean_names() %>%
  mutate(year = ifelse(year == 1, 2023,
                ifelse(year == 2, 2022,
                ifelse(year == 3, 2019, 
                ifelse(year == 4, 2018, 2017)))))
#clean skaters
reg_merged$skater <- gsub('[0-9]', '', reg_merged$skater)
reg_merged$skater <- str_squish(reg_merged$skater)

reg_merged %>% 
  kable() %>% 
  kable_styling(full_width = F) %>% 
  scroll_box(width = "100%", height = "200px")

year	final_rank	skater	nation	sp_score	sp_rank	fs_score	fs_rank	total_score	competition	sex
2023	1	Kaori Sakamoto	JP	71.72	1	145.89	1	217.61	GPUSA	Female
2023	2	Isabeau Levito	US	71.30	2	135.36	2	206.66	GPUSA	Female
2023	3	Amber Glenn	US	68.42	3	129.19	3	197.61	GPUSA	Female
2023	4	Haein Lee	KR	66.24	4	113.26	5	179.50	GPUSA	Female
2023	5	Ekaterina Kurakova	PL	63.65	6	115.03	4	178.68	GPUSA	Female
2023	6	Gracie Gold	US	64.18	5	109.91	6	174.09	GPUSA	Female
2023	7	Nicole Schott	DE	56.47	10	103.88	8	160.35	GPUSA	Female
2023	8	Yeonjeong Park	KR	60.04	7	98.54	9	158.58	GPUSA	Female
2023	9	Ahsun Yun	KR	47.98	11	108.72	7	156.70	GPUSA	Female
2023	10	Eliska Brezinova	CZ	56.65	9	96.92	10	153.57	GPUSA	Female
2023	11	Marilena Kitromilis	CY	46.01	12	89.47	11	135.48	GPUSA	Female
2023	NA	Rino Matsuike	JP	59.50	8	NA	NA	NA	GPUSA	Female
2023	1	Ilia Malinin	US	86.08	4	194.29	1	280.37	GPUSA	Male
2023	2	Kao Miura	JP	94.96	1	178.23	2	273.19	GPUSA	Male
2023	3	Junhwan Cha	KR	94.44	2	169.61	3	264.05	GPUSA	Male
2023	4	Daniel Grassl	IT	88.43	3	169.25	4	257.68	GPUSA	Male
2023	5	Roman Sadovsky	CA	78.15	5	147.26	7	225.41	GPUSA	Male
2023	6	Wesley Chiu	CA	71.58	9	148.32	6	219.90	GPUSA	Male
2023	7	Liam Kapeikis	US	74.29	8	145.21	8	219.50	GPUSA	Male
2023	8	Sena Miyake	JP	77.87	6	137.87	9	215.74	GPUSA	Male
2023	9	Koshiro Shimada	JP	62.54	12	152.58	5	215.12	GPUSA	Male
2023	10	Dinh Tran	US	64.99	11	134.69	10	199.68	GPUSA	Male
2023	11	Mihhail Selevko	EE	75.75	7	116.05	12	191.80	GPUSA	Male
2023	12	Donovan Carrillo	MX	69.18	10	119.10	11	188.28	GPUSA	Male
2023	1	Yelim Kim	KR	72.22	1	132.27	2	204.49	GPJPN	Female
2023	2	Kaori Sakamoto	JP	68.07	2	133.80	1	201.87	GPJPN	Female
2023	3	Rion Sumiyoshi	JP	68.01	3	125.11	4	193.12	GPJPN	Female
2023	4	Audrey Shin	US	65.87	4	123.13	5	189.00	GPJPN	Female
2023	5	Rinka Watanabe	JP	58.36	9	129.71	3	188.07	GPJPN	Female
2023	6	Seoyeon Ji	KR	62.92	6	121.22	7	184.14	GPJPN	Female
2023	7	Niina Petrõkina	EE	58.81	8	121.48	6	180.29	GPJPN	Female
2023	8	Seoyeong Wi	KR	61.06	7	115.68	10	176.74	GPJPN	Female
2023	9	Starr Andrews	US	64.13	5	109.93	12	174.06	GPJPN	Female
2023	10	Olga Mikutina	AT	56.95	10	116.41	9	173.36	GPJPN	Female
2023	11	Amber Glenn	US	52.04	11	117.32	8	169.36	GPJPN	Female
2023	12	Eva Lotta Kiibus	EE	48.56	12	113.81	11	162.37	GPJPN	Female
2023	1	Shoma Uno	JP	91.66	2	188.10	1	279.76	GPJPN	Male
2023	2	Sota Yamamoto	JP	96.49	1	161.36	6	257.85	GPJPN	Male
2023	3	Junhwan Cha	KR	80.35	6	174.41	2	254.76	GPJPN	Male
2023	4	Kazuki Tomono	JP	85.07	4	166.76	3	251.83	GPJPN	Male
2023	5	Adam Siao Him Fa	FR	87.44	3	163.01	4	250.45	GPJPN	Male
2023	6	Matteo Rizzo	IT	78.57	7	162.19	5	240.76	GPJPN	Male
2023	7	Nika Egadze	GE	84.47	5	148.39	8	232.86	GPJPN	Male
2023	8	Stephen Gogolev	CA	69.01	9	152.01	7	221.02	GPJPN	Male
2023	9	Gabriele Frangipani	IT	68.78	10	143.53	9	212.31	GPJPN	Male
2023	10	Conrad Orzel	CA	73.10	8	129.59	11	202.69	GPJPN	Male
2023	11	Maurizio Zandrón	AT	68.21	11	133.51	10	201.72	GPJPN	Male
2023	12	Tomoki Hiwatashi	US	57.18	12	127.87	12	185.05	GPJPN	Male
2023	1	Mai Mihara	JP	72.23	1	145.20	1	217.43	GPGBR	Female
2023	2	Isabeau Levito	US	72.06	2	143.68	2	215.74	GPGBR	Female
2023	3	Anastasiia Gubanova	GE	66.82	3	126.29	5	193.11	GPGBR	Female
2023	4	Young You	KR	61.21	6	130.15	3	191.36	GPGBR	Female
2023	5	Ekaterina Kurakova	PL	63.46	4	126.98	4	190.44	GPGBR	Female
2023	6	Nicole Schott	DE	60.38	7	121.03	6	181.41	GPGBR	Female
2023	7	Gabriella Izzo	US	62.92	5	111.18	7	174.10	GPGBR	Female
2023	8	Gabrielle Daleman	CA	58.95	8	104.82	8	163.77	GPGBR	Female
2023	9	Alexia Paganini	CH	54.63	11	102.26	10	156.89	GPGBR	Female
2023	10	Julia Sauter	RO	52.38	12	104.08	9	156.46	GPGBR	Female
2023	11	Natasha Mckay	GB	57.62	9	97.58	11	155.20	GPGBR	Female
2023	12	Bradie Tennell	US	56.50	10	96.69	12	153.19	GPGBR	Female
2023	1	Daniel Grassl	IT	86.85	2	177.50	1	264.35	GPGBR	Male
2023	2	Deniss Vasiljevs	LV	83.01	3	171.55	2	254.56	GPGBR	Male
2023	3	Shun Sato	JP	82.68	4	166.35	3	249.03	GPGBR	Male
2023	4	Koshiro Shimada	JP	80.84	5	166.33	4	247.17	GPGBR	Male
2023	5	Tatsuya Tsuboi	JP	76.75	7	149.38	5	226.13	GPGBR	Male
2023	6	Roman Sadovsky	CA	89.49	1	129.86	8	219.35	GPGBR	Male
2023	7	Jimmy Ma	US	77.72	6	136.75	7	214.47	GPGBR	Male
2023	8	Morisi Kvitelashvili	GE	56.42	12	138.83	6	195.25	GPGBR	Male
2023	9	Tomoki Hiwatashi	US	66.68	8	122.05	9	188.73	GPGBR	Male
2023	10	Corey Circelli	CA	62.97	10	119.84	10	182.81	GPGBR	Male
2023	11	Graham Newberry	GB	64.30	9	116.12	12	180.42	GPGBR	Male
2023	12	Edward Appleby	GB	62.52	11	117.61	11	180.13	GPGBR	Male
2023	1	Loena Hendrickx	BE	72.75	1	143.59	1	216.34	GPFRA	Female
2023	2	Yelim Kim	KR	68.93	2	125.83	4	194.76	GPFRA	Female
2023	3	Rion Sumiyoshi	JP	64.10	5	130.24	3	194.34	GPFRA	Female
2023	4	Haein Lee	KR	62.77	6	130.72	2	193.49	GPFRA	Female
2023	5	Audrey Shin	US	64.27	4	119.66	5	183.93	GPFRA	Female
2023	6	Mana Kawabe	JP	68.83	3	113.67	8	182.50	GPFRA	Female
2023	7	Rino Matsuike	JP	57.68	9	118.84	6	176.52	GPFRA	Female
2023	8	Maé-Bérénice Méité	FR	58.84	8	116.84	7	175.68	GPFRA	Female
2023	9	Léa Serna	FR	62.63	7	105.26	9	167.89	GPFRA	Female
2023	10	Olga Mikutina	AT	56.00	10	103.99	10	159.99	GPFRA	Female
2023	11	Lindsay Van Zundert	NL	55.11	11	98.98	11	154.09	GPFRA	Female
2023	12	Maïa Mazzara	FR	46.05	12	94.80	12	140.85	GPFRA	Female
2023	1	Adam Siao Him Fa	FR	88.00	3	180.98	1	268.98	GPFRA	Male
2023	2	Sota Yamamoto	JP	92.42	1	165.48	3	257.90	GPFRA	Male
2023	3	Kazuki Tomono	JP	89.46	2	159.31	4	248.77	GPFRA	Male
2023	4	Sihyeong Lee	KR	76.54	7	166.08	2	242.62	GPFRA	Male
2023	5	Nika Egadze	GE	82.44	4	150.96	6	233.40	GPFRA	Male
2023	6	Luc Economides	FR	77.23	6	152.41	5	229.64	GPFRA	Male
2023	7	Lukas Britschgi	CH	74.25	9	148.61	7	222.86	GPFRA	Male
2023	8	Ivan Shmuratko	UA	75.95	8	144.13	8	220.08	GPFRA	Male
2023	9	Mihhail Selevko	EE	79.40	5	133.52	11	212.92	GPFRA	Male
2023	10	Wesley Chiu	CA	67.95	11	142.00	10	209.95	GPFRA	Male
2023	11	Landry Le May	FR	60.87	12	142.52	9	203.39	GPFRA	Male
2023	NA	Sena Miyake	JP	69.27	10	NA	NA	NA	GPFRA	Male
2023	1	Mai Mihara	JP	73.58	2	130.56	1	204.14	GPFIN	Female
2023	2	Loena Hendrickx	BE	74.88	1	129.03	3	203.91	GPFIN	Female
2023	3	Mana Kawabe	JP	67.03	3	130.38	2	197.41	GPFIN	Female
2023	4	Rika Kihira	JP	64.07	6	128.36	4	192.43	GPFIN	Female
2023	5	Madeline Schizas	CA	65.19	5	122.65	5	187.84	GPFIN	Female
2023	6	Lindsay Thorngren	US	65.75	4	117.48	6	183.23	GPFIN	Female
2023	7	Anastasiia Gubanova	GE	56.03	9	110.54	8	166.57	GPFIN	Female
2023	8	Bradie Tennell	US	60.64	7	103.34	9	163.98	GPFIN	Female
2023	9	Jenni Saarinen	FI	59.69	8	95.95	11	155.64	GPFIN	Female
2023	10	Janna Jyrkinen	FI	42.89	12	111.56	7	154.45	GPFIN	Female
2023	11	Linnea Ceder	FI	55.63	10	96.28	10	151.91	GPFIN	Female
2023	12	Eva Lotta Kiibus	EE	49.27	11	89.62	12	138.89	GPFIN	Female
2023	1	Ilia Malinin	US	85.57	2	192.82	1	278.39	GPFIN	Male
2023	2	Shun Sato	JP	81.59	3	180.62	2	262.21	GPFIN	Male
2023	3	Kévin Aymoz	FR	88.96	1	166.73	3	255.69	GPFIN	Male
2023	4	Tatsuya Tsuboi	JP	78.82	5	166.08	4	244.90	GPFIN	Male
2023	5	Camden Pulkinen	US	72.45	7	157.47	5	229.92	GPFIN	Male
2023	6	Nikolaj Majorov	SE	69.94	8	139.61	6	209.55	GPFIN	Male
2023	7	Arlet Levandi	EE	72.67	6	136.83	7	209.50	GPFIN	Male
2023	8	Keegan Messing	CA	80.12	4	124.90	12	205.02	GPFIN	Male
2023	9	Valtter Virtanen	FI	69.15	9	134.87	8	204.02	GPFIN	Male
2023	10	Aleksandr Selevko	EE	66.96	11	132.51	10	199.47	GPFIN	Male
2023	11	Lucas Tsuyoshi Honda	JP	67.92	10	129.98	11	197.90	GPFIN	Male
2023	12	Morisi Kvitelashvili	GE	62.42	12	134.38	9	196.80	GPFIN	Male
2023	1	Mai Mihara	JP	74.58	2	133.59	1	208.17	GPF	Female
2023	2	Isabeau Levito	US	69.26	5	127.97	2	197.23	GPF	Female
2023	3	Loena Hendrickx	BE	74.24	3	122.11	4	196.35	GPF	Female
2023	4	Rinka Watanabe	JP	72.58	4	123.43	3	196.01	GPF	Female
2023	5	Kaori Sakamoto	JP	75.86	1	116.70	6	192.56	GPF	Female
2023	6	Yelim Kim	KR	61.55	6	119.03	5	180.58	GPF	Female
2023	1	Shoma Uno	JP	99.99	1	204.47	1	304.46	GPF	Male
2023	2	Sota Yamamoto	JP	94.86	2	179.49	3	274.35	GPF	Male
2023	3	Ilia Malinin	US	80.10	5	191.84	2	271.94	GPF	Male
2023	4	Shun Sato	JP	76.62	6	173.54	4	250.16	GPF	Male
2023	5	Kao Miura	JP	87.07	3	158.67	6	245.74	GPF	Male
2023	6	Daniel Grassl	IT	80.40	4	164.57	5	244.97	GPF	Male
2023	1	Rinka Watanabe	JP	63.27	6	134.32	1	197.59	GPCAN	Female
2023	2	Starr Andrews	US	64.69	5	126.57	2	191.26	GPCAN	Female
2023	3	Young You	KR	65.10	4	125.05	4	190.15	GPCAN	Female
2023	4	Ava Marie Ziegler	US	66.49	3	120.27	7	186.76	GPCAN	Female
2023	5	Rika Kihira	JP	59.27	8	125.06	3	184.33	GPCAN	Female
2023	6	Niina Petrõkina	EE	61.68	7	119.66	8	181.34	GPCAN	Female
2023	7	Madeline Schizas	CA	67.90	1	112.69	9	180.59	GPCAN	Female
2023	8	Yuhana Yokoi	JP	54.87	12	123.86	5	178.73	GPCAN	Female
2023	9	Lindsay Thorngren	US	55.16	10	120.93	6	176.09	GPCAN	Female
2023	10	Gabrielle Daleman	CA	66.65	2	104.96	11	171.61	GPCAN	Female
2023	11	Lindsay Van Zundert	NL	55.22	9	105.74	10	160.96	GPCAN	Female
2023	12	Eliska Brezinova	CZ	55.14	11	103.89	12	159.03	GPCAN	Female
2023	1	Shoma Uno	JP	89.98	2	183.17	1	273.15	GPCAN	Male
2023	2	Kao Miura	JP	94.06	1	171.23	2	265.29	GPCAN	Male
2023	3	Matteo Rizzo	IT	81.18	3	169.85	4	251.03	GPCAN	Male
2023	4	Keegan Messing	CA	79.69	4	171.03	3	250.72	GPCAN	Male
2023	5	Camden Pulkinen	US	75.07	5	143.99	8	219.06	GPCAN	Male
2023	6	Lukas Britschgi	CH	64.35	8	148.08	6	212.43	GPCAN	Male
2023	7	Stephen Gogolev	CA	57.94	11	152.70	5	210.64	GPCAN	Male
2023	8	Aleksandr Selevko	EE	60.37	10	145.74	7	206.11	GPCAN	Male
2023	9	Jimmy Ma	US	61.73	9	142.66	9	204.39	GPCAN	Male
2023	10	Deniss Vasiljevs	LV	69.01	7	128.44	10	197.45	GPCAN	Male
2023	11	Conrad Orzel	CA	69.69	6	125.73	11	195.42	GPCAN	Male
2023	1	Anastasiia Gubanova	GE	69.81	1	130.10	1	199.91	EC	Female
2023	2	Loena Hendrickx	BE	67.85	2	125.63	3	193.48	EC	Female
2023	3	Kimmy Repond	CH	63.83	3	128.68	2	192.51	EC	Female
2023	4	Ekaterina Kurakova	PL	61.81	5	125.09	4	186.90	EC	Female
2023	5	Nina Pinzarrone	BE	61.35	6	124.57	5	185.92	EC	Female
2023	6	Niina Petrõkina	EE	61.05	7	122.69	6	183.74	EC	Female
2023	7	Janna Jyrkinen	FI	60.77	8	116.19	7	176.96	EC	Female
2023	8	Lara Naki Gutmann	IT	55.39	13	113.90	8	169.29	EC	Female
2023	9	Nicole Schott	DE	54.33	16	109.49	9	163.82	EC	Female
2023	10	Julia Sauter	RO	56.58	11	103.84	12	160.42	EC	Female
2023	11	Sofja Stepchenko	LV	55.32	14	104.02	11	159.34	EC	Female
2023	12	Olga Mikutina	AT	62.78	4	96.30	18	159.08	EC	Female
2023	13	Marilena Kitromilis	CY	53.71	18	105.20	10	158.91	EC	Female
2023	14	Lindsay Van Zundert	NL	58.13	10	99.97	15	158.10	EC	Female
2023	15	Eva Lotta Kiibus	EE	55.26	15	101.69	13	156.95	EC	Female
2023	16	Alexandra Feigin	BG	54.31	17	100.92	14	155.23	EC	Female
2023	17	Josefin Taljegård	SE	55.53	12	99.45	16	154.98	EC	Female
2023	18	Livia Kaiser	CH	60.25	9	90.95	20	151.20	EC	Female
2023	19	Natasha Mckay	GB	51.94	20	96.06	19	148.00	EC	Female
2023	20	Anastasia Gozhva	UA	46.78	22	96.91	17	143.69	EC	Female
2023	21	Daša Grm	SI	52.47	19	90.58	21	143.05	EC	Female
2023	22	Mia Caroline Risa Gomez	NO	49.14	21	88.48	22	137.62	EC	Female
2023	23	Júlia Láng	HU	46.33	23	83.95	23	130.28	EC	Female
2023	24	Nikola Rychtaříková	CZ	45.64	24	77.49	24	123.13	EC	Female
2023	25	Alexandra Michaela Filcová	SK	43.94	25	NA	NA	NA	EC	Female
2023	26	Léa Serna	FR	43.93	26	NA	NA	NA	EC	Female
2023	27	Antonina Dubinina	RS	42.51	27	NA	NA	NA	EC	Female
2023	28	Anastasia Gracheva	MD	39.08	28	NA	NA	NA	EC	Female
2023	29	Alexandra Mintsidou	GR	33.86	29	NA	NA	NA	EC	Female
2023	1	Adam Siao Him Fa	FR	96.53	1	171.24	2	267.77	EC	Male
2023	2	Matteo Rizzo	IT	86.46	2	173.46	1	259.92	EC	Male
2023	3	Lukas Britschgi	CH	79.26	5	168.75	3	248.01	EC	Male
2023	4	Kévin Aymoz	FR	83.75	4	157.17	4	240.92	EC	Male
2023	5	Deniss Vasiljevs	LV	84.81	3	151.54	6	236.35	EC	Male
2023	6	Daniel Grassl	IT	77.03	8	153.80	5	230.83	EC	Male
2023	7	Nika Egadze	GE	72.96	12	147.69	7	220.65	EC	Male
2023	8	Mihhail Selevko	EE	73.74	11	144.56	8	218.30	EC	Male
2023	9	Andreas Nordebäck	SE	75.98	9	136.97	10	212.95	EC	Male
2023	10	Gabriele Frangipani	IT	77.35	7	134.27	12	211.62	EC	Male
2023	11	Maurizio Zandrón	AT	72.57	13	135.11	11	207.68	EC	Male
2023	12	Tomás Guarino Sabaté	ES	71.65	14	133.54	13	205.19	EC	Male
2023	13	Mark Gorodnitsky	IL	64.94	22	137.40	9	202.34	EC	Male
2023	14	Valtter Virtanen	FI	68.33	18	129.95	14	198.28	EC	Male
2023	15	Nikita Starostin	DE	74.70	10	123.27	17	197.97	EC	Male
2023	16	Morisi Kvitelashvili	GE	70.55	16	124.04	16	194.59	EC	Male
2023	17	Vladimir Samoilov	PL	78.26	6	113.33	21	191.59	EC	Male
2023	18	Adam Hagara	SK	65.15	21	124.57	15	189.72	EC	Male
2023	19	Jari Kessler	HR	67.87	19	114.96	19	182.83	EC	Male
2023	20	Burak Demirboga	TR	64.33	23	118.49	18	182.82	EC	Male
2023	21	Kyrylo Marsak	UA	70.41	17	111.57	22	181.98	EC	Male
2023	22	Davidé Lewton Brain	MC	66.07	20	113.47	20	179.54	EC	Male
2023	23	Graham Newberry	GB	70.85	15	103.79	24	174.64	EC	Male
2023	24	Aleksandr Vlasenko	HU	62.49	24	111.45	23	173.94	EC	Male
2023	25	Petr Kotlarik	CZ	60.24	25	NA	NA	NA	EC	Male
2023	26	Georgiy Reshtenko	CZ	54.52	26	NA	NA	NA	EC	Male
2023	27	Larry Loupolover	BG	53.26	27	NA	NA	NA	EC	Male
2023	28	Samuel Mcallister	IE	48.07	28	NA	NA	NA	EC	Male
2023	29	David Sedej	SI	46.28	29	NA	NA	NA	EC	Male
2023	1	Haein Lee	KR	69.13	6	141.71	1	210.84	4CC	Female
2023	2	Yelim Kim	KR	72.84	1	136.45	3	209.29	4CC	Female
2023	3	Mone Chiba	JP	67.28	7	137.70	2	204.98	4CC	Female
2023	4	Chaeyeon Kim	KR	71.39	3	131.00	5	202.39	4CC	Female
2023	5	Rinka Watanabe	JP	65.60	8	134.90	4	200.50	4CC	Female
2023	6	Bradie Tennell	US	69.49	5	130.42	6	199.91	4CC	Female
2023	7	Amber Glenn	US	69.63	4	122.87	8	192.50	4CC	Female
2023	8	Hana Yoshida	JP	59.82	10	129.78	7	189.60	4CC	Female
2023	9	Sara-Maude Dupuis	CA	51.68	12	118.99	9	170.67	4CC	Female
2023	10	Madeline Schizas	CA	60.11	9	99.62	10	159.73	4CC	Female
2023	11	Justine Miclette	CA	51.24	13	98.32	11	149.56	4CC	Female
2023	12	Jocelyn Hong	NZ	52.02	11	96.31	12	148.33	4CC	Female
2023	13	Tzu-Han Ting	TW	45.19	17	95.32	13	140.51	4CC	Female
2023	14	Tara Prasad	IN	46.04	14	87.11	14	133.15	4CC	Female
2023	15	Joanna So	HK	45.90	15	87.09	15	132.99	4CC	Female
2023	16	Anna Levkovets	KZ	45.53	16	83.73	16	129.26	4CC	Female
2023	17	Sofia Farafonova	KZ	44.66	18	81.82	17	126.48	4CC	Female
2023	18	Vlada Vasiliev	AU	40.13	21	73.82	18	113.95	4CC	Female
2023	19	Cheuk Ka Kahlen Cheung	HK	39.58	22	64.80	19	104.38	4CC	Female
2023	20	Hiu Yau Chow	HK	42.10	20	58.06	21	100.16	4CC	Female
2023	21	Bagdana Rakhishova	KZ	35.96	23	59.62	20	95.58	4CC	Female
2023	NA	Isabeau Levito	US	71.50	2	NA	NA	NA	4CC	Female
2023	NA	Sofia Lexi Jacqueline Frank	PH	43.82	19	NA	NA	NA	4CC	Female
2023	1	Kao Miura	JP	91.90	1	189.63	1	281.53	4CC	Male
2023	2	Keegan Messing	CA	86.70	2	188.87	2	275.57	4CC	Male
2023	3	Shun Sato	JP	80.81	6	178.33	3	259.14	4CC	Male
2023	4	Junhwan Cha	KR	83.77	5	166.37	4	250.14	4CC	Male
2023	5	Mikhail Shaidorov	KZ	72.43	12	164.71	5	237.14	4CC	Male
2023	6	Sihyeong Lee	KR	70.38	14	157.41	6	227.79	4CC	Male
2023	7	Boyang Jin	CN	85.32	4	142.15	10	227.47	4CC	Male
2023	8	Conrad Orzel	CA	80.09	7	146.01	7	226.10	4CC	Male
2023	9	Jimmy Ma	US	86.64	3	134.40	13	221.04	4CC	Male
2023	10	Maxim Naumov	US	75.96	8	142.75	9	218.71	4CC	Male
2023	11	Koshiro Shimada	JP	74.06	10	143.79	8	217.85	4CC	Male
2023	12	Jaeseok Kyeong	KR	75.30	9	136.68	12	211.98	4CC	Male
2023	13	Stephen Gogolev	CA	72.82	11	136.94	11	209.76	4CC	Male
2023	14	Liam Kapeikis	US	71.43	13	126.57	14	198.00	4CC	Male
2023	15	Yudong Chen	CN	67.93	15	116.48	16	184.41	4CC	Male
2023	16	Dias Jirenbayev	KZ	57.67	18	125.75	15	183.42	4CC	Male
2023	17	Edrian Paul Célestino	PH	66.83	16	100.09	19	166.92	4CC	Male
2023	18	Darian Kaptich	AU	58.22	17	107.91	17	166.13	4CC	Male
2023	19	Rakhat Bralin	KZ	54.25	19	105.95	18	160.20	4CC	Male
2023	20	Pagiel Yie Ken Sng	SG	48.52	20	90.31	20	138.83	4CC	Male
2023	21	Douglas Gerber	NZ	40.75	21	85.20	21	125.95	4CC	Male
2023	22	Lap Kan Lincoln Yuen	HK	36.33	22	80.71	22	117.04	4CC	Male
2022	1	Anna Shcherbakova	RU	80.20	2	175.75	2	255.95	OLY	Female
2022	2	Alexandra Trusova	RU	74.60	4	177.13	1	251.73	OLY	Female
2022	3	Kaori Sakamoto	JP	79.84	3	153.29	3	233.13	OLY	Female
2022	4	Kamila Valieva	RU	82.16	1	141.93	5	224.09	OLY	Female
2022	5	Wakaba Higuchi	JP	73.51	5	140.93	6	214.44	OLY	Female
2022	6	Young You	KR	70.34	6	142.75	4	213.09	OLY	Female
2022	7	Alysa Liu	US	69.50	8	139.45	7	208.95	OLY	Female
2022	8	Loena Hendrickx	BE	70.09	7	136.70	9	206.79	OLY	Female
2022	9	Yelim Kim	KR	67.78	9	134.85	11	202.63	OLY	Female
2022	10	Mariah Bell	US	65.38	11	136.92	8	202.30	OLY	Female
2022	11	Anastasiia Gubanova	GE	65.40	10	135.58	10	200.98	OLY	Female
2022	12	Ekaterina Kurakova	PL	59.08	24	126.76	12	185.84	OLY	Female
2022	13	Viktoriia Safonova	BY	61.46	17	123.37	13	184.83	OLY	Female
2022	14	Olga Mikutina	AT	61.14	18	121.06	14	182.20	OLY	Female
2022	15	Ekaterina Ryabova	AZ	61.82	16	118.15	15	179.97	OLY	Female
2022	16	Karen Chen	US	64.11	13	115.82	17	179.93	OLY	Female
2022	17	Nicole Schott	DE	63.13	14	114.52	19	177.65	OLY	Female
2022	18	Lindsay Van Zundert	NL	59.24	22	116.57	16	175.81	OLY	Female
2022	19	Madeline Schizas	CA	60.53	20	115.03	18	175.56	OLY	Female
2022	20	Eliska Brezinova	CZ	64.31	12	111.10	21	175.41	OLY	Female
2022	21	Eva Lotta Kiibus	EE	59.55	21	112.20	20	171.75	OLY	Female
2022	22	Alexia Paganini	CH	61.06	19	107.85	22	168.91	OLY	Female
2022	23	Mana Kawabe	JP	62.69	15	104.04	23	166.73	OLY	Female
2022	24	Alexandra Feigin	BG	59.16	23	100.15	24	159.31	OLY	Female
2022	25	Jenni Saarinen	FI	56.97	25	96.07	25	153.04	OLY	Female
2022	26	Josefin Taljegård	SE	54.51	26	NA	NA	NA	OLY	Female
2022	27	Yi Zhu	CN	53.44	27	NA	NA	NA	OLY	Female
2022	28	Natasha Mckay	GB	52.54	28	NA	NA	NA	OLY	Female
2022	29	Kailani Craine	AU	49.93	29	NA	NA	NA	OLY	Female
2022	30	Anastasiia Shabotova	UA	48.68	30	NA	NA	NA	OLY	Female
2022	1	Nathan Chen	US	113.97	1	218.63	1	332.60	OLY	Male
2022	2	Yuma Kagiyama	JP	108.12	2	201.93	2	310.05	OLY	Male
2022	3	Shoma Uno	JP	105.90	3	187.10	5	293.00	OLY	Male
2022	4	Yuzuru Hanyu	JP	95.15	8	188.06	3	283.21	OLY	Male
2022	5	Junhwan Cha	KR	99.51	4	182.87	7	282.38	OLY	Male
2022	6	Jason Brown	US	97.24	6	184.00	6	281.24	OLY	Male
2022	7	Daniel Grassl	IT	90.64	12	187.43	4	278.07	OLY	Male
2022	8	Evgeni Semenenko	RU	95.76	7	178.37	9	274.13	OLY	Male
2022	9	Boyang Jin	CN	90.98	11	179.45	8	270.43	OLY	Male
2022	10	Morisi Kvitelashvili	GE	97.98	5	170.64	11	268.62	OLY	Male
2022	11	Keegan Messing	CA	93.24	9	172.37	10	265.61	OLY	Male
2022	12	Kévin Aymoz	FR	93.00	10	161.80	15	254.80	OLY	Male
2022	13	Deniss Vasiljevs	LV	85.30	16	167.41	12	252.71	OLY	Male
2022	14	Adam Siao Him Fa	FR	86.74	14	163.41	13	250.15	OLY	Male
2022	15	Mark Kondratiuk	RU	86.11	15	162.71	14	248.82	OLY	Male
2022	16	Matteo Rizzo	IT	88.63	13	158.90	17	247.53	OLY	Male
2022	17	Brendan Kerry	AU	84.79	17	160.01	16	244.80	OLY	Male
2022	18	Vladimir Litvintsev	AZ	84.15	18	155.04	19	239.19	OLY	Male
2022	19	Andrei Mozalev	RU	77.05	23	156.28	18	233.33	OLY	Male
2022	20	Konstantin Milyukov	BY	78.49	21	143.73	20	222.22	OLY	Male
2022	21	Nikolaj Majorov	SE	78.54	20	142.24	21	220.78	OLY	Male
2022	22	Donovan Carrillo	MX	79.69	19	138.44	22	218.13	OLY	Male
2022	23	Lukas Britschgi	CH	76.16	24	136.42	23	212.58	OLY	Male
2022	24	Ivan Shmuratko	UA	78.11	22	127.65	24	205.76	OLY	Male
2022	25	Michal Březina	CZ	75.19	25	NA	NA	NA	OLY	Male
2022	26	Alexei Bychenko	IL	68.01	26	NA	NA	NA	OLY	Male
2022	27	Sihyeong Lee	KR	65.69	27	NA	NA	NA	OLY	Male
2022	28	Aleksandr Selevko	EE	65.29	28	NA	NA	NA	OLY	Male
2022	29	Roman Sadovsky	CA	62.77	29	NA	NA	NA	OLY	Male
2022	1	Alexandra Trusova	RU	77.69	1	154.68	1	232.37	GPUSA	Female
2022	2	Daria Usacheva	RU	76.71	2	140.60	4	217.31	GPUSA	Female
2022	3	Young You	KR	70.73	5	146.24	2	216.97	GPUSA	Female
2022	4	Kaori Sakamoto	JP	71.16	4	144.77	3	215.93	GPUSA	Female
2022	5	Ksenia Sinitsina	RU	71.51	3	134.25	5	205.76	GPUSA	Female
2022	6	Amber Glenn	US	67.57	7	133.45	7	201.02	GPUSA	Female
2022	7	Satoko Miyahara	JP	66.36	8	134.15	6	200.51	GPUSA	Female
2022	8	Yelim Kim	KR	70.56	6	128.78	8	199.34	GPUSA	Female
2022	9	Ekaterina Kurakova	PL	61.36	11	127.24	9	188.60	GPUSA	Female
2022	10	Starr Andrews	US	61.94	10	115.69	11	177.63	GPUSA	Female
2022	11	Yuhana Yokoi	JP	54.77	12	119.30	10	174.07	GPUSA	Female
2022	12	Audrey Shin	US	62.82	9	97.96	12	160.78	GPUSA	Female
2022	1	Vincent Zhou	US	97.43	1	198.13	1	295.56	GPUSA	Male
2022	2	Shoma Uno	JP	89.07	2	181.61	3	270.68	GPUSA	Male
2022	3	Nathan Chen	US	82.89	4	186.48	2	269.37	GPUSA	Male
2022	4	Shun Sato	JP	80.52	5	166.53	4	247.05	GPUSA	Male
2022	5	Jimmy Ma	US	84.52	3	143.60	10	228.12	GPUSA	Male
2022	6	Michal Březina	CZ	75.43	6	152.04	5	227.47	GPUSA	Male
2022	7	Daniel Grassl	IT	70.88	8	150.55	6	221.43	GPUSA	Male
2022	8	Nam Nguyen	CA	74.32	7	145.28	9	219.60	GPUSA	Male
2022	9	Adam Siao Him Fa	FR	67.60	10	149.92	7	217.52	GPUSA	Male
2022	10	Artur Danielian	RU	68.74	9	146.19	8	214.93	GPUSA	Male
2022	NA	Kévin Aymoz	FR	58.14	11	NA	NA	NA	GPUSA	Male
2022	1	Kamila Valieva	RU	87.42	1	185.29	1	272.71	GPRUS	Female
2022	2	Elizaveta Tuktamysheva	RU	80.10	2	149.13	3	229.23	GPRUS	Female
2022	3	Maiia Khromykh	RU	64.72	5	154.97	2	219.69	GPRUS	Female
2022	4	Mariah Bell	US	69.37	3	140.98	4	210.35	GPRUS	Female
2022	5	Loena Hendrickx	BE	64.44	6	139.25	5	203.69	GPRUS	Female
2022	6	Madeline Schizas	CA	67.82	4	124.32	7	192.14	GPRUS	Female
2022	7	Viktoriia Safonova	BY	58.19	9	127.45	6	185.64	GPRUS	Female
2022	8	Rino Matsuike	JP	62.98	7	121.38	8	184.36	GPRUS	Female
2022	9	Ekaterina Kurakova	PL	56.43	11	119.21	9	175.64	GPRUS	Female
2022	10	Ekaterina Ryabova	AZ	58.87	8	116.37	10	175.24	GPRUS	Female
2022	11	Eva Lotta Kiibus	EE	49.26	12	113.85	11	163.11	GPRUS	Female
2022	12	Olga Mikutina	AT	57.09	10	104.00	12	161.09	GPRUS	Female
2022	1	Morisi Kvitelashvili	GE	95.37	2	170.96	3	266.33	GPRUS	Male
2022	2	Mikhail Kolyada	RU	84.48	4	180.16	1	264.64	GPRUS	Male
2022	3	Kazuki Tomono	JP	95.81	1	168.38	5	264.19	GPRUS	Male
2022	4	Roman Sadovsky	CA	84.59	3	169.21	4	253.80	GPRUS	Male
2022	5	Matteo Rizzo	IT	77.45	9	173.02	2	250.47	GPRUS	Male
2022	6	Evgeni Semenenko	RU	81.00	7	165.66	6	246.66	GPRUS	Male
2022	7	Camden Pulkinen	US	83.47	5	154.50	9	237.97	GPRUS	Male
2022	8	Mark Kondratiuk	RU	74.16	11	157.72	8	231.88	GPRUS	Male
2022	9	Keiji Tanaka	JP	76.69	10	153.06	10	229.75	GPRUS	Male
2022	10	Michal Březina	CZ	82.31	6	137.28	11	219.59	GPRUS	Male
2022	11	Nika Egadze	GE	50.35	12	159.82	7	210.17	GPRUS	Male
2022	12	Brendan Kerry	AU	80.48	8	123.71	12	204.19	GPRUS	Male
2022	1	Kaori Sakamoto	JP	76.56	1	146.78	1	223.34	GPJPN	Female
2022	2	Mana Kawabe	JP	73.88	2	131.56	4	205.44	GPJPN	Female
2022	3	Young You	KR	68.08	3	135.52	2	203.60	GPJPN	Female
2022	4	Alysa Liu	US	67.72	4	135.18	3	202.90	GPJPN	Female
2022	5	Eunsoo Lim	KR	65.23	5	121.45	6	186.68	GPJPN	Female
2022	6	Rino Matsuike	JP	63.34	7	122.83	5	186.17	GPJPN	Female
2022	7	Amber Glenn	US	63.43	6	112.40	8	175.83	GPJPN	Female
2022	8	Nicole Schott	DE	59.26	8	113.11	7	172.37	GPJPN	Female
2022	9	Seoyeong Wi	KR	58.23	9	112.31	9	170.54	GPJPN	Female
2022	1	Shoma Uno	JP	102.58	1	187.57	1	290.15	GPJPN	Male
2022	2	Vincent Zhou	US	99.51	2	161.18	6	260.69	GPJPN	Male
2022	3	Junhwan Cha	KR	95.92	3	163.68	5	259.60	GPJPN	Male
2022	4	Makar Ignatov	RU	90.54	4	166.66	4	257.20	GPJPN	Male
2022	5	Matteo Rizzo	IT	84.78	6	171.06	3	255.84	GPJPN	Male
2022	6	Alexander Samarin	RU	84.32	7	171.33	2	255.65	GPJPN	Male
2022	7	Sota Yamamoto	JP	86.05	5	152.85	8	238.90	GPJPN	Male
2022	8	Kao Miura	JP	76.62	8	156.27	7	232.89	GPJPN	Male
2022	9	Tomoki Hiwatashi	US	72.36	9	144.72	9	217.08	GPJPN	Male
2022	10	Nam Nguyen	CA	64.28	10	144.11	10	208.39	GPJPN	Male
2022	11	Camden Pulkinen	US	55.53	11	137.65	11	193.18	GPJPN	Male
2022	1	Anna Shcherbakova	RU	71.73	3	165.05	1	236.78	GPITA	Female
2022	2	Maiia Khromykh	RU	72.04	2	154.31	2	226.35	GPITA	Female
2022	3	Loena Hendrickx	BE	73.52	1	145.53	3	219.05	GPITA	Female
2022	4	Mai Mihara	JP	70.46	5	144.49	4	214.95	GPITA	Female
2022	5	Satoko Miyahara	JP	70.85	4	138.72	5	209.57	GPITA	Female
2022	6	Yelim Kim	KR	62.78	7	130.72	6	193.50	GPITA	Female
2022	7	Sofia Samodurova	RU	58.68	9	121.91	7	180.59	GPITA	Female
2022	8	Eunsoo Lim	KR	67.03	6	112.55	8	179.58	GPITA	Female
2022	9	Yi Zhu	CN	60.00	8	111.25	9	171.25	GPITA	Female
2022	10	Nicole Schott	DE	58.33	10	108.87	10	167.20	GPITA	Female
2022	11	Lara Naki Gutmann	IT	54.83	11	103.74	11	158.57	GPITA	Female
2022	12	Lucrezia Beccari	IT	53.35	12	94.94	12	148.29	GPITA	Female
2022	1	Yuma Kagiyama	JP	80.53	7	197.49	1	278.02	GPITA	Male
2022	2	Mikhail Kolyada	RU	92.30	4	181.25	2	273.55	GPITA	Male
2022	3	Daniel Grassl	IT	95.67	2	173.33	3	269.00	GPITA	Male
2022	4	Deniss Vasiljevs	LV	85.09	5	163.35	4	248.44	GPITA	Male
2022	5	Junhwan Cha	KR	95.56	3	152.18	6	247.74	GPITA	Male
2022	6	Kazuki Tomono	JP	83.91	6	161.20	5	245.11	GPITA	Male
2022	7	Boyang Jin	CN	97.89	1	144.38	9	242.27	GPITA	Male
2022	8	Peter Gumennik	RU	76.81	9	149.95	7	226.76	GPITA	Male
2022	9	Dmitri Aliev	RU	71.07	10	146.60	8	217.67	GPITA	Male
2022	10	Yudong Chen	CN	78.79	8	122.17	10	200.96	GPITA	Male
2022	11	Gabriele Frangipani	IT	55.09	12	112.51	11	167.60	GPITA	Male
2022	NA	Paul Fentz	DE	57.17	11	NA	NA	NA	GPITA	Male
2022	1	Anna Shcherbakova	RU	77.94	1	151.75	1	229.69	GPFRA	Female
2022	2	Alena Kostornaia	RU	76.44	2	145.41	2	221.85	GPFRA	Female
2022	3	Wakaba Higuchi	JP	63.87	6	141.04	3	204.91	GPFRA	Female
2022	4	Ksenia Sinitsina	RU	69.89	3	128.87	6	198.76	GPFRA	Female
2022	5	Karen Chen	US	64.67	5	129.33	5	194.00	GPFRA	Female
2022	6	Mariah Bell	US	60.81	10	129.98	4	190.79	GPFRA	Female
2022	7	Ekaterina Ryabova	AZ	63.34	7	123.31	8	186.65	GPFRA	Female
2022	8	Yeonjeong Park	KR	67.00	4	119.11	9	186.11	GPFRA	Female
2022	9	Yuhana Yokoi	JP	52.32	11	124.61	7	176.93	GPFRA	Female
2022	10	Haein Lee	KR	63.18	8	108.14	10	171.32	GPFRA	Female
2022	11	Léa Serna	FR	62.75	9	107.58	11	170.33	GPFRA	Female
2022	1	Yuma Kagiyama	JP	100.64	1	185.77	1	286.41	GPFRA	Male
2022	2	Shun Sato	JP	87.82	4	177.17	3	264.99	GPFRA	Male
2022	3	Jason Brown	US	89.39	3	174.81	4	264.20	GPFRA	Male
2022	4	Deniss Vasiljevs	LV	89.76	2	164.72	7	254.48	GPFRA	Male
2022	5	Dmitri Aliev	RU	85.05	5	168.51	5	253.56	GPFRA	Male
2022	6	Keegan Messing	CA	85.03	6	168.03	6	253.06	GPFRA	Male
2022	7	Andrei Mozalev	RU	68.77	9	179.77	2	248.54	GPFRA	Male
2022	8	Adam Siao Him Fa	FR	84.47	7	158.82	9	243.29	GPFRA	Male
2022	9	Kévin Aymoz	FR	63.98	12	164.10	8	228.08	GPFRA	Male
2022	10	Artur Danielian	RU	76.81	8	144.69	11	221.50	GPFRA	Male
2022	11	Romain Ponsart	FR	66.38	10	145.89	10	212.27	GPFRA	Male
2022	12	Gabriele Frangipani	IT	66.33	11	117.94	12	184.27	GPFRA	Male
2022	1	Kamila Valieva	RU	84.19	1	180.89	1	265.08	GPCAN	Female
2022	2	Elizaveta Tuktamysheva	RU	81.24	2	151.64	2	232.88	GPCAN	Female
2022	3	Alena Kostornaia	RU	75.58	3	138.96	4	214.54	GPCAN	Female
2022	4	Mai Mihara	JP	67.89	7	142.12	3	210.01	GPCAN	Female
2022	5	Alysa Liu	US	73.63	4	132.90	7	206.53	GPCAN	Female
2022	6	Wakaba Higuchi	JP	69.41	5	135.86	5	205.27	GPCAN	Female
2022	7	Haein Lee	KR	62.63	8	127.37	8	190.00	GPCAN	Female
2022	8	Madeline Schizas	CA	62.61	9	123.95	9	186.56	GPCAN	Female
2022	9	Mana Kawabe	JP	53.30	12	133.22	6	186.52	GPCAN	Female
2022	10	Karen Chen	US	68.74	6	114.67	10	183.41	GPCAN	Female
2022	11	Emily Bausback	CA	59.53	10	100.35	11	159.88	GPCAN	Female
2022	12	Alison Schumacher	CA	55.47	11	95.72	12	151.19	GPCAN	Female
2022	1	Nathan Chen	US	106.72	1	200.46	1	307.18	GPCAN	Male
2022	2	Jason Brown	US	94.00	2	165.55	3	259.55	GPCAN	Male
2022	3	Evgeni Semenenko	RU	87.71	5	168.30	2	256.01	GPCAN	Male
2022	4	Makar Ignatov	RU	89.79	4	154.38	5	244.17	GPCAN	Male
2022	5	Keegan Messing	CA	93.28	3	145.06	10	238.34	GPCAN	Male
2022	6	Morisi Kvitelashvili	GE	71.60	12	161.27	4	232.87	GPCAN	Male
2022	7	Sota Yamamoto	JP	78.78	7	146.96	8	225.74	GPCAN	Male
2022	8	Alexander Samarin	RU	78.55	8	145.65	9	224.20	GPCAN	Male
2022	9	Conrad Orzel	CA	73.19	9	149.56	6	222.75	GPCAN	Male
2022	10	Keiji Tanaka	JP	78.83	6	143.37	12	222.20	GPCAN	Male
2022	11	Tomoki Hiwatashi	US	72.92	11	148.85	7	221.77	GPCAN	Male
2022	12	Roman Sadovsky	CA	72.94	10	144.79	11	217.73	GPCAN	Male
2022	1	Kamila Valieva	RU	90.45	1	168.61	1	259.06	EC	Female
2022	2	Anna Shcherbakova	RU	69.05	4	168.37	2	237.42	EC	Female
2022	3	Alexandra Trusova	RU	75.13	3	159.23	3	234.36	EC	Female
2022	4	Loena Hendrickx	BE	76.25	2	131.72	5	207.97	EC	Female
2022	5	Ekaterina Kurakova	PL	67.47	5	137.26	4	204.73	EC	Female
2022	6	Ekaterina Ryabova	AZ	65.47	7	131.28	6	196.75	EC	Female
2022	7	Anastasiia Gubanova	GE	67.02	6	121.15	9	188.17	EC	Female
2022	8	Niina Petrõkina	EE	58.30	17	128.77	7	187.07	EC	Female
2022	9	Viktoriia Safonova	BY	63.07	8	122.34	8	185.41	EC	Female
2022	10	Alexia Paganini	CH	62.32	9	115.78	10	178.10	EC	Female
2022	11	Eva Lotta Kiibus	EE	59.16	15	112.48	11	171.64	EC	Female
2022	12	Léa Serna	FR	62.16	10	108.84	13	171.00	EC	Female
2022	13	Nicole Schott	DE	61.86	11	108.32	14	170.18	EC	Female
2022	14	Josefin Taljegård	SE	58.24	18	106.06	15	164.30	EC	Female
2022	15	Olga Mikutina	AT	60.16	12	103.85	17	164.01	EC	Female
2022	16	Lara Naki Gutmann	IT	52.94	23	111.05	12	163.99	EC	Female
2022	17	Natasha Mckay	GB	57.07	19	104.67	16	161.74	EC	Female
2022	18	Jenni Saarinen	FI	58.93	16	101.39	19	160.32	EC	Female
2022	19	Yasmine Kimiko Yamada	CH	56.54	21	102.64	18	159.18	EC	Female
2022	20	Alexandra Feigin	BG	56.78	20	98.78	20	155.56	EC	Female
2022	21	Eliska Brezinova	CZ	59.62	13	95.62	21	155.24	EC	Female
2022	22	Aleksandra Golovkina	LT	52.63	24	89.57	22	142.20	EC	Female
2022	23	Regina Schermann	HU	54.43	22	78.99	23	133.42	EC	Female
2022	NA	Marina Piredda	IT	59.53	14	NA	NA	NA	EC	Female
2022	25	Anete Lace	LV	49.75	25	NA	NA	NA	EC	Female
2022	26	Oona Ounasvuori	FI	49.13	26	NA	NA	NA	EC	Female
2022	27	Lindsay Van Zundert	NL	48.92	27	NA	NA	NA	EC	Female
2022	28	Daša Grm	SI	47.85	28	NA	NA	NA	EC	Female
2022	29	Antonina Dubinina	RS	47.77	29	NA	NA	NA	EC	Female
2022	30	Taylor Lynn Morris	IL	46.60	30	NA	NA	NA	EC	Female
2022	31	Linnea Kilsand	NO	45.51	31	NA	NA	NA	EC	Female
2022	32	Marilena Kitromilis	CY	44.03	32	NA	NA	NA	EC	Female
2022	33	Alexandra Michaela Filcová	SK	43.56	33	NA	NA	NA	EC	Female
2022	34	Aldís Kara Bergsdóttír	IS	42.23	34	NA	NA	NA	EC	Female
2022	35	Sinem Pekder	TR	42.16	35	NA	NA	NA	EC	Female
2022	36	Maia Sørensen	DK	40.93	36	NA	NA	NA	EC	Female
2022	1	Mark Kondratiuk	RU	99.06	2	187.50	1	286.56	EC	Male
2022	2	Daniel Grassl	IT	91.75	5	182.73	2	274.48	EC	Male
2022	3	Deniss Vasiljevs	LV	90.24	6	181.84	3	272.08	EC	Male
2022	4	Andrei Mozalev	RU	99.76	1	165.93	6	265.69	EC	Male
2022	5	Evgeni Semenenko	RU	99.04	3	160.96	9	260.00	EC	Male
2022	6	Morisi Kvitelashvili	GE	92.76	4	161.15	8	253.91	EC	Male
2022	7	Kévin Aymoz	FR	80.39	10	171.82	4	252.21	EC	Male
2022	8	Vladimir Litvintsev	AZ	83.46	7	161.24	7	244.70	EC	Male
2022	9	Gabriele Frangipani	IT	81.79	9	157.16	10	238.95	EC	Male
2022	10	Michal Březina	CZ	71.60	15	166.78	5	238.38	EC	Male
2022	11	Lukas Britschgi	CH	72.96	13	145.95	11	218.91	EC	Male
2022	12	Ivan Shmuratko	UA	82.13	8	132.44	15	214.57	EC	Male
2022	13	Nikita Starostin	DE	72.12	14	142.28	12	214.40	EC	Male
2022	14	Arlet Levandi	EE	70.04	17	138.48	13	208.52	EC	Male
2022	15	Nikolaj Memola	IT	73.98	12	132.55	14	206.53	EC	Male
2022	16	Paul Fentz	DE	76.76	11	129.30	16	206.06	EC	Male
2022	17	Maurizio Zandrón	AT	70.75	16	123.16	20	193.91	EC	Male
2022	18	Kornel Witkowski	PL	66.26	23	127.03	17	193.29	EC	Male
2022	19	Valtter Virtanen	FI	67.34	20	123.63	18	190.97	EC	Male
2022	20	Davidé Lewton Brain	MC	67.31	21	123.36	19	190.67	EC	Male
2022	21	Konstantin Milyukov	BY	69.10	18	113.49	21	182.59	EC	Male
2022	22	Tomás Guarino Sabaté	ES	66.20	24	112.47	22	178.67	EC	Male
2022	23	Burak Demirboga	TR	67.30	22	100.73	23	168.03	EC	Male
2022	24	Slavik Hayrapetyan	AM	67.75	19	100.09	24	167.84	EC	Male
2022	25	Adam Hagara	SK	65.23	25	NA	NA	NA	EC	Male
2022	26	Graham Newberry	GB	64.49	26	NA	NA	NA	EC	Male
2022	27	Matyas Belohradsky	CZ	64.38	27	NA	NA	NA	EC	Male
2022	28	Nika Egadze	GE	63.60	28	NA	NA	NA	EC	Male
2022	29	Daniels Kockers	LV	56.10	29	NA	NA	NA	EC	Male
2022	30	Conor Stakelum	IE	56.00	30	NA	NA	NA	EC	Male
2022	31	Jari Kessler	HR	55.82	31	NA	NA	NA	EC	Male
2022	32	Andras Csernoch	HU	54.88	32	NA	NA	NA	EC	Male
2022	33	Larry Loupolover	BG	45.67	33	NA	NA	NA	EC	Male
2022	1	Mai Mihara	JP	72.62	1	145.41	1	218.03	4CC	Female
2022	2	Haein Lee	KR	69.97	2	143.55	2	213.52	4CC	Female
2022	3	Yelim Kim	KR	68.93	3	140.98	4	209.91	4CC	Female
2022	4	Audrey Shin	US	67.20	5	136.66	5	203.86	4CC	Female
2022	5	Rino Matsuike	JP	60.16	8	142.05	3	202.21	4CC	Female
2022	6	Young You	KR	67.86	4	130.70	7	198.56	4CC	Female
2022	7	Yuhana Yokoi	JP	53.93	12	131.41	6	185.34	4CC	Female
2022	8	Gabriella Izzo	US	63.19	7	116.87	8	180.06	4CC	Female
2022	9	Starr Andrews	US	66.60	6	106.41	12	173.01	4CC	Female
2022	10	Gabrielle Daleman	CA	59.01	9	113.97	9	172.98	4CC	Female
2022	11	Alison Schumacher	CA	57.36	11	111.06	10	168.42	4CC	Female
2022	12	Kailani Craine	AU	57.46	10	106.56	11	164.02	4CC	Female
2022	13	Véronik Mallet	CA	53.77	13	98.10	13	151.87	4CC	Female
2022	14	Jocelyn Hong	NZ	48.60	18	97.02	14	145.62	4CC	Female
2022	15	Tzu-Han Ting	TW	49.15	17	96.42	15	145.57	4CC	Female
2022	16	Sofia Lexi Jacqueline Frank	PH	52.74	14	86.52	17	139.26	4CC	Female
2022	17	Victoria Alcantara	AU	49.73	16	88.53	16	138.26	4CC	Female
2022	18	Andrea Montesinos Cantú	MX	47.36	19	85.67	18	133.03	4CC	Female
2022	19	Eugenia Garza Martinez	MX	51.16	15	77.57	20	128.73	4CC	Female
2022	20	Tara Prasad	IN	43.31	20	84.62	19	127.93	4CC	Female
2022	1	Junhwan Cha	KR	98.96	1	174.26	1	273.22	4CC	Male
2022	2	Kazuki Tomono	JP	97.10	2	171.89	2	268.99	4CC	Male
2022	3	Kao Miura	JP	88.37	3	162.70	3	251.07	4CC	Male
2022	4	Sena Miyake	JP	79.67	5	160.35	4	240.02	4CC	Male
2022	5	Mikhail Shaidorov	KZ	75.96	8	158.71	5	234.67	4CC	Male
2022	6	Brendan Kerry	AU	81.12	4	146.45	8	227.57	4CC	Male
2022	7	Sihyeong Lee	KR	79.13	6	144.05	11	223.18	4CC	Male
2022	8	Tomoki Hiwatashi	US	77.51	7	144.86	10	222.37	4CC	Male
2022	9	Joseph Phan	CA	69.70	10	151.15	6	220.85	4CC	Male
2022	10	Jimmy Ma	US	69.98	9	145.14	9	215.12	4CC	Male
2022	11	Corey Circelli	CA	69.57	11	143.45	12	213.02	4CC	Male
2022	12	Camden Pulkinen	US	57.58	14	146.81	7	204.39	4CC	Male
2022	13	Dias Jirenbayev	KZ	66.92	12	126.00	13	192.92	4CC	Male
2022	14	Jaeseok Kyeong	KR	63.78	13	124.19	14	187.97	4CC	Male
2022	15	James Min	AU	54.35	15	100.67	15	155.02	4CC	Male
2022	16	Jordan Dodds	AU	47.47	16	91.68	16	139.15	4CC	Male
2022	NA	Harrison Jon Yen Wong	HK	43.95	17	NA	NA	NA	4CC	Male
2019	12	Yi Christy Leung	HK	53.93	15	110.86	11	164.79	4CC	Female
2019	8	Yelim Kim	KR	64.42	9	123.51	7	187.93	4CC	Female
2019	3	Vincent Zhou	US	100.18	1	172.04	5	272.22	4CC	Male
2019	9	Véronik Mallet	CA	54.97	12	115.49	9	170.46	4CC	Female
2019	8	Tomoki Hiwatashi	US	76.95	9	159.84	7	236.79	4CC	Male
2019	11	Ting Cui	US	66.73	7	98.11	14	164.84	4CC	Female
2019	15	Sihyeong Lee	KR	56.03	21	127.95	13	183.98	4CC	Male
2019	1	Shoma Uno	JP	91.76	4	197.36	1	289.12	4CC	Male
2019	1	Rika Kihira	JP	68.85	5	153.14	1	221.99	4CC	Female
2019	23	Nikita Manko	KZ	51.15	22	87.25	23	138.40	4CC	Male
2019	11	Nicolas Nadeau	CA	74.44	11	135.21	11	209.65	4CC	Male
2019	10	Nam Nguyen	CA	79.55	8	136.94	10	216.49	4CC	Male
2019	18	Micah Tang	TW	62.95	17	111.64	19	174.59	4CC	Male
2019	19	Micah Kai Lynette	TH	57.45	19	116.99	16	174.44	4CC	Male
2019	24	Mark Webster	AU	50.24	24	76.17	24	126.41	4CC	Male
2019	6	Mariah Bell	US	70.02	3	123.92	6	193.94	4CC	Female
2019	3	Mai Mihara	JP	65.15	8	141.97	2	207.12	4CC	Female
2019	22	Leslie Man Cheuk Ip	HK	50.40	23	96.20	21	146.60	4CC	Male
2019	10	Larkyn Austman	CA	54.99	11	110.22	12	165.21	4CC	Female
2019	7	Keiji Tanaka	JP	83.93	7	167.61	6	251.54	4CC	Male
2019	4	Keegan Messing	CA	88.18	5	179.43	3	267.61	4CC	Male
2019	12	Kazuki Tomono	JP	74.16	12	132.25	12	206.41	4CC	Male
2019	4	Kaori Sakamoto	JP	73.36	2	133.43	4	206.79	4CC	Female
2019	15	Kailani Craine	AU	60.64	10	88.88	17	149.52	4CC	Female
2019	25	Kai Xiang Chew	MY	46.38	25	NA	NA	NA	4CC	Male
2019	6	Junhwan Cha	KR	97.33	2	158.50	8	255.83	4CC	Male
2019	14	Junehyoung Lee	KR	64.19	16	123.91	14	188.10	4CC	Male
2019	20	Julian Zhi-Jie Yee	MY	61.23	18	112.87	17	174.10	4CC	Male
2019	21	Joanna So	HK	50.00	18	67.82	21	117.82	4CC	Female
2019	5	Jason Brown	US	86.57	6	172.32	4	258.89	4CC	Male
2019	17	Isadora Williams	BR	47.92	19	90.34	16	138.26	4CC	Female
2019	14	Hongyi Chen	CN	54.44	13	96.06	15	150.50	4CC	Female
2019	16	He Zhang	CN	67.38	15	112.51	18	179.89	4CC	Male
2019	21	Harrison Jon Yen Wong	HK	56.27	20	92.27	22	148.54	4CC	Male
2019	13	Hanul Kim	KR	51.44	17	111.04	10	162.48	4CC	Female
2019	7	Eunsoo Lim	KR	69.14	4	122.71	8	191.85	4CC	Female
2019	2	Elizabet Tursynbayeva	KZ	68.09	6	139.37	3	207.46	4CC	Female
2019	17	Donovan Carrillo	MX	71.16	14	103.54	20	174.70	4CC	Male
2019	NA	Brooklee Han	AU	54.22	14	NA	NA	NA	4CC	Female
2019	9	Brendan Kerry	AU	76.81	10	147.63	9	224.44	4CC	Male
2019	5	Bradie Tennell	US	73.91	1	128.16	5	202.07	4CC	Female
2019	2	Boyang Jin	CN	92.17	3	181.34	2	273.51	4CC	Male
2019	13	Andrew Dodds	AU	71.91	13	119.49	15	191.40	4CC	Male
2019	20	Andrea Montesinos Cantú	MX	42.92	22	81.59	20	124.51	4CC	Female
2019	19	Amy Lin	TW	46.99	20	87.57	18	134.56	4CC	Female
2019	18	Alisson Perticheto	PH	51.66	16	85.31	19	136.97	4CC	Female
2019	16	Alaine Chartrand	CA	45.89	21	101.65	13	147.54	4CC	Female
2019	15	Yasmine Kimiko Yamada	CH	51.21	18	99.91	14	151.12	EC	Female
2019	30	Yakau Zenko	BY	56.38	30	NA	NA	NA	EC	Male
2019	16	Vladimir Litvintsev	AZ	73.60	14	130.68	15	204.28	EC	Male
2019	3	Viveca Lindfors	FI	65.61	4	128.79	3	194.40	EC	Female
2019	31	Valentina Matos	ES	42.86	31	NA	NA	NA	EC	Female
2019	36	Thomas Kennes	NL	48.57	36	NA	NA	NA	EC	Male
2019	4	Stanislava Konstantinova	RU	56.76	11	132.96	2	189.72	EC	Female
2019	26	Sophia Schaller	AT	44.20	26	NA	NA	NA	EC	Female
2019	18	Sondre Oddvoll Bøe	NO	66.03	20	125.98	18	192.01	EC	Male
2019	1	Sofia Samodurova	RU	72.88	2	140.96	1	213.84	EC	Female
2019	26	Slavik Hayrapetyan	AM	59.87	26	NA	NA	NA	EC	Male
2019	30	Silvia Hugec	SK	43.33	30	NA	NA	NA	EC	Female
2019	32	Roman Galay	FI	55.40	32	NA	NA	NA	EC	Male
2019	23	Pernille Sørensen	DK	50.59	20	81.19	23	131.78	EC	Female
2019	32	Paulina Ramanauskaite	LT	42.31	32	NA	NA	NA	EC	Female
2019	15	Paul Fentz	DE	69.70	17	140.26	12	209.96	EC	Male
2019	27	Nikolaj Majorov	SE	59.68	27	NA	NA	NA	EC	Male
2019	16	Nicole Schott	DE	50.68	19	98.58	16	149.26	EC	Female
2019	9	Nicole Rajičová	SK	64.08	5	104.95	12	169.03	EC	Female
2019	29	Nicky-Leo Obreykov	BG	56.54	29	NA	NA	NA	EC	Male
2019	21	Nathalie Weinzierl	DE	46.09	24	88.49	21	134.58	EC	Female
2019	20	Natasha Mckay	GB	48.20	22	91.88	19	140.08	EC	Female
2019	10	Morisi Kvitelashvili	GE	73.04	15	146.75	7	219.79	EC	Male
2019	5	Mikhail Kolyada	RU	100.49	1	140.38	11	240.87	EC	Male
2019	7	Michal Březina	CZ	83.66	8	150.59	6	234.25	EC	Male
2019	35	Michael Neuman	SK	53.38	35	NA	NA	NA	EC	Male
2019	14	Maxim Kovtun	RU	87.70	5	128.48	16	216.18	EC	Male
2019	19	Matyas Belohradsky	CZ	67.40	18	123.82	21	191.22	EC	Male
2019	3	Matteo Rizzo	IT	81.41	10	165.67	3	247.08	EC	Male
2019	7	Maé-Bérénice Méité	FR	58.95	8	118.15	5	177.10	EC	Female
2019	31	Lukas Britschgi	CH	55.86	31	NA	NA	NA	EC	Male
2019	19	Lucrezia Gennaro	IT	52.91	16	90.19	20	143.10	EC	Female
2019	20	Luc Maierhofer	AT	63.63	21	125.37	19	189.00	EC	Male
2019	5	Laurine Lecavelier	FR	63.29	6	116.76	6	180.05	EC	Female
2019	29	Lara Naki Gutmann	IT	43.96	29	NA	NA	NA	EC	Female
2019	28	Kyarha Van Tiel	NL	44.00	28	NA	NA	NA	EC	Female
2019	4	Kévin Aymoz	FR	88.02	4	158.32	4	246.34	EC	Male
2019	14	Julia Sauter	RO	54.29	14	98.86	15	153.15	EC	Female
2019	1	Javier Fernández	ES	91.84	3	179.75	1	271.59	EC	Male
2019	13	Ivett Tóth	HU	54.90	13	105.93	10	160.83	EC	Female
2019	22	Ivan Shmuratko	UA	67.26	19	111.03	24	178.29	EC	Male
2019	23	Irakli Maysuradze	GE	60.89	24	113.54	22	174.43	EC	Male
2019	25	Ihor Reznichenko	PL	59.99	25	NA	NA	NA	EC	Male
2019	34	Héctor Alonso Serrano	ES	53.94	34	NA	NA	NA	EC	Male
2019	34	Hana Cvijanović	HR	38.00	34	NA	NA	NA	EC	Female
2019	21	Graham Newberry	GB	61.33	22	127.29	17	188.62	EC	Male
2019	27	Gerli Liinamäe	EE	44.08	27	NA	NA	NA	EC	Female
2019	8	Emmi Peltonen	FI	58.06	10	111.97	8	170.03	EC	Female
2019	25	Elzbieta Gabryszak	PL	45.77	25	NA	NA	NA	EC	Female
2019	35	Elizabete Jubkane	LV	37.75	35	NA	NA	NA	EC	Female
2019	10	Eliska Brezinova	CZ	55.85	12	110.92	9	166.77	EC	Female
2019	12	Ekaterina Ryabova	AZ	59.95	7	103.22	13	163.17	EC	Female
2019	11	Deniss Vasiljevs	LV	78.87	12	140.63	10	219.50	EC	Male
2019	24	Davidé Lewton Brain	MC	61.07	23	112.54	23	173.61	EC	Male
2019	17	Daša Grm	SI	53.50	15	93.79	17	147.29	EC	Female
2019	13	Daniel Samohin	IL	86.48	6	130.69	14	217.17	EC	Male
2019	6	Daniel Grassl	IT	81.69	9	155.01	5	236.70	EC	Male
2019	33	Conor Stakelum	IE	55.03	33	NA	NA	NA	EC	Male
2019	33	Camila Gjersem	NO	39.81	33	NA	NA	NA	EC	Female
2019	28	Burak Demirboga	TR	56.95	28	NA	NA	NA	EC	Male
2019	24	Antonina Dubinina	RS	47.20	23	73.05	24	120.25	EC	Female
2019	18	Anita Östlund	SE	52.76	17	91.90	18	144.66	EC	Female
2019	22	Anastasiya Galustyan	AM	48.38	21	84.25	22	132.63	EC	Female
2019	36	Anastasia Gozhva	UA	35.51	36	NA	NA	NA	EC	Female
2019	2	Alina Zagitova	RU	75.00	1	123.34	4	198.34	EC	Female
2019	6	Alexia Paganini	CH	65.64	3	114.26	7	179.90	EC	Female
2019	9	Alexei Bychenko	IL	84.19	7	136.31	13	220.50	EC	Male
2019	11	Alexandra Feigin	BG	58.80	9	105.40	11	164.20	EC	Female
2019	2	Alexander Samarin	RU	91.97	2	177.87	2	269.84	EC	Male
2019	8	Alexander Majorov	SE	79.88	11	145.50	8	225.38	EC	Male
2019	37	Alexander Borovoj	HU	46.56	37	NA	NA	NA	EC	Male
2019	17	Aleksandr Selevko	EE	69.94	16	125.19	20	195.13	EC	Male
2019	12	Adam Siao Him Fa	FR	76.70	13	141.36	9	218.06	EC	Male
2019	11	Yura Matsuda	JP	53.35	10	104.24	10	157.59	GPCAN	Female
2019	6	Wakaba Higuchi	JP	66.51	2	114.78	7	181.29	GPCAN	Female
2019	7	Starr Andrews	US	64.77	4	109.95	9	174.72	GPCAN	Female
2019	1	Shoma Uno	JP	88.87	2	188.38	1	277.25	GPCAN	Male
2019	12	Roman Sadovsky	CA	67.72	12	142.88	8	210.60	GPCAN	Male
2019	5	Nam Nguyen	CA	82.22	7	158.72	5	240.94	GPCAN	Male
2019	4	Mariah Bell	US	63.35	5	126.90	4	190.25	GPCAN	Female
2019	2	Mako Yamashita	JP	66.30	3	136.76	2	203.06	GPCAN	Female
2019	7	Kévin Aymoz	FR	78.83	10	151.26	7	230.09	GPCAN	Male
2019	2	Keegan Messing	CA	95.05	1	170.12	2	265.17	GPCAN	Male
2019	9	Kazuki Tomono	JP	81.63	8	139.20	10	220.83	GPCAN	Male
2019	3	Junhwan Cha	KR	88.86	3	165.91	3	254.77	GPCAN	Male
2019	6	Jason Brown	US	76.46	11	158.51	6	234.97	GPCAN	Male
2019	3	Evgenia Medvedeva	RU	60.83	7	137.08	1	197.91	GPCAN	Female
2019	1	Elizaveta Tuktamysheva	RU	74.22	1	129.10	3	203.32	GPCAN	Female
2019	5	Elizabet Tursynbayeva	KZ	61.19	6	124.52	5	185.71	GPCAN	Female
2019	9	Daria Panenkova	RU	51.41	11	117.13	6	168.54	GPCAN	Female
2019	8	Daniel Samohin	IL	84.90	5	140.99	9	225.89	GPCAN	Male
2019	11	Brendan Kerry	AU	80.99	9	139.09	11	220.08	GPCAN	Male
2019	10	Alicia Pineault	CA	59.02	9	99.27	11	158.29	GPCAN	Female
2019	4	Alexander Samarin	RU	88.06	4	160.72	4	248.78	GPCAN	Male
2019	10	Alexander Majorov	SE	84.64	6	135.66	12	220.30	GPCAN	Male
2019	8	Alaine Chartrand	CA	60.47	8	111.70	8	172.17	GPCAN	Female
2019	5	Sofia Samodurova	RU	68.24	5	136.09	5	204.33	GPF	Female
2019	2	Shoma Uno	JP	91.67	2	183.43	2	275.10	GPF	Male
2019	6	Sergei Voronov	RU	82.96	5	143.48	6	226.44	GPF	Male
2019	6	Satoko Miyahara	JP	67.52	6	133.79	6	201.31	GPF	Female
2019	1	Rika Kihira	JP	82.51	1	150.61	1	233.12	GPF	Female
2019	1	Nathan Chen	US	92.99	1	189.43	1	282.42	GPF	Male
2019	4	Michal Březina	CZ	89.21	3	166.05	4	255.26	GPF	Male
2019	5	Keegan Messing	CA	79.56	6	156.49	5	236.05	GPF	Male
2019	4	Kaori Sakamoto	JP	70.23	4	141.45	4	211.68	GPF	Female
2019	3	Junhwan Cha	KR	89.07	4	174.42	3	263.49	GPF	Male
2019	3	Elizaveta Tuktamysheva	RU	70.65	3	144.67	3	215.32	GPF	Female
2019	2	Alina Zagitova	RU	77.93	2	148.60	2	226.53	GPF	Female
2019	1	Yuzuru Hanyu	JP	106.69	1	190.43	1	297.12	GPFIN	Male
2019	4	Yuna Shiraiwa	JP	63.77	2	127.69	5	191.46	GPFIN	Female
2019	8	Viveca Lindfors	FI	52.95	10	106.67	6	159.62	GPFIN	Female
2019	11	Valtter Virtanen	FI	48.16	11	106.58	11	154.74	GPFIN	Male
2019	2	Stanislava Konstantinova	RU	62.56	4	135.01	3	197.57	GPFIN	Female
2019	10	Rika Hongo	JP	51.11	11	105.48	7	156.59	GPFIN	Female
2019	10	Phillip Harris	GB	58.99	10	123.67	10	182.66	GPFIN	Male
2019	4	Mikhail Kolyada	RU	81.76	6	157.03	4	238.79	GPFIN	Male
2019	2	Michal Březina	CZ	93.31	2	164.67	2	257.98	GPFIN	Male
2019	5	Loena Hendrickx	BE	63.17	3	128.05	4	191.22	GPFIN	Female
2019	8	Keiji Tanaka	JP	80.60	7	126.22	9	206.82	GPFIN	Male
2019	3	Kaori Sakamoto	JP	57.26	7	140.16	2	197.42	GPFIN	Female
2019	3	Junhwan Cha	KR	82.82	4	160.37	3	243.19	GPFIN	Male
2019	7	Hanul Kim	KR	55.38	8	104.77	8	160.15	GPFIN	Female
2019	9	Emmi Peltonen	FI	59.90	5	98.82	10	158.72	GPFIN	Female
2019	6	Daria Panenkova	RU	58.23	6	103.25	9	161.48	GPFIN	Female
2019	5	Boyang Jin	CN	85.97	3	141.31	5	227.28	GPFIN	Male
2019	11	Angela Wang	US	53.76	9	95.81	11	149.57	GPFIN	Female
2019	6	Andrei Lazukin	RU	82.54	5	135.68	7	218.22	GPFIN	Male
2019	1	Alina Zagitova	RU	68.90	1	146.39	1	215.29	GPFIN	Female
2019	7	Alexei Krasnozhon	US	74.05	8	136.98	6	211.03	GPFIN	Male
2019	9	Alexei Bychenko	IL	73.44	9	128.89	8	202.33	GPFIN	Male
2019	6	Romain Ponsart	FR	84.97	4	144.89	6	229.86	GPFRA	Male
2019	NA	Nicolas Nadeau	CA	61.46	11	NA	NA	NA	GPFRA	Male
2019	1	Nathan Chen	US	86.94	3	184.64	1	271.58	GPFRA	Male
2019	5	Kévin Aymoz	FR	81.00	6	150.16	5	231.16	GPFRA	Male
2019	8	Keiji Tanaka	JP	79.35	8	136.97	8	216.32	GPFRA	Male
2019	2	Jason Brown	US	96.41	1	159.92	3	256.33	GPFRA	Male
2019	4	Dmitri Aliev	RU	75.15	9	162.67	2	237.82	GPFRA	Male
2019	7	Deniss Vasiljevs	LV	82.30	5	138.96	7	221.26	GPFRA	Male
2019	10	Daniel Samohin	IL	72.33	10	133.66	9	205.99	GPFRA	Male
2019	9	Boyang Jin	CN	79.41	7	129.48	10	208.89	GPFRA	Male
2019	3	Alexander Samarin	RU	90.86	2	156.23	4	247.09	GPFRA	Male
2019	12	Yaroslav Paniot	UA	68.59	9	105.05	12	173.64	GPJPN	Male
2019	4	Vincent Zhou	US	75.90	5	147.52	4	223.42	GPJPN	Male
2019	6	Sota Yamamoto	JP	74.98	6	138.42	5	213.40	GPJPN	Male
2019	1	Shoma Uno	JP	92.49	1	183.96	1	276.45	GPJPN	Male
2019	2	Sergei Voronov	RU	91.37	2	162.91	2	254.28	GPJPN	Male
2019	2	Satoko Miyahara	JP	76.08	2	143.39	2	219.47	GPJPN	Female
2019	1	Rika Kihira	JP	69.59	5	154.72	1	224.31	GPJPN	Female
2019	3	Matteo Rizzo	IT	77.00	4	147.71	3	224.71	GPJPN	Male
2019	5	Mariah Bell	US	62.97	7	135.99	4	198.96	GPJPN	Female
2019	9	Maria Sotskova	RU	60.75	9	116.24	9	176.99	GPJPN	Female
2019	4	Mai Mihara	JP	70.38	3	133.82	5	204.20	GPJPN	Female
2019	10	Maé-Bérénice Méité	FR	50.49	12	112.09	10	162.58	GPJPN	Female
2019	11	Kevin Reynolds	CA	61.14	12	121.53	10	182.67	GPJPN	Male
2019	12	Kailani Craine	AU	58.21	11	96.01	12	154.22	GPJPN	Female
2019	9	Junehyoung Lee	KR	66.16	11	122.10	9	188.26	GPJPN	Male
2019	10	Hiroaki Sato	JP	67.38	10	117.80	11	185.18	GPJPN	Male
2019	6	Eunsoo Lim	KR	69.78	4	126.53	6	196.31	GPJPN	Female
2019	3	Elizaveta Tuktamysheva	RU	76.17	1	142.85	3	219.02	GPJPN	Female
2019	5	Dmitri Aliev	RU	81.16	3	138.36	6	219.52	GPJPN	Male
2019	8	Deniss Vasiljevs	LV	72.39	7	125.21	8	197.60	GPJPN	Male
2019	8	Courtney Hicks	US	59.10	10	118.97	8	178.07	GPJPN	Female
2019	11	Angela Wang	US	60.82	8	98.54	11	159.36	GPJPN	Female
2019	7	Alexander Johnson	US	72.03	8	127.72	7	199.75	GPJPN	Male
2019	7	Alena Leonova	RU	68.22	6	125.93	7	194.15	GPJPN	Female
2019	1	Yuzuru Hanyu	JP	110.53	1	167.89	1	278.42	GPRUS	Male
2019	9	Yura Matsuda	JP	52.00	8	85.99	9	137.99	GPRUS	Female
2019	5	Yuna Shiraiwa	JP	60.35	5	120.58	4	180.93	GPRUS	Female
2019	2	Sofia Samodurova	RU	67.40	2	130.61	2	198.01	GPRUS	Female
2019	8	Polina Tsurskaya	RU	56.81	7	92.64	8	149.45	GPRUS	Female
2019	6	Paul Fentz	DE	78.28	5	142.29	7	220.57	GPRUS	Male
2019	2	Morisi Kvitelashvili	GE	89.94	2	158.64	2	248.58	GPRUS	Male
2019	4	Mikhail Kolyada	RU	69.10	8	156.32	4	225.42	GPRUS	Male
2019	7	Mako Yamashita	JP	51.00	9	110.22	7	161.22	GPRUS	Female
2019	5	Keegan Messing	CA	73.83	7	146.92	6	220.75	GPRUS	Male
2019	3	Kazuki Tomono	JP	82.26	4	156.47	3	238.73	GPRUS	Male
2019	12	Julian Zhi-Jie Yee	MY	60.37	12	118.34	12	178.71	GPRUS	Male
2019	NA	Gracie Gold	US	37.51	10	NA	NA	NA	GPRUS	Female
2019	3	Eunsoo Lim	KR	57.76	6	127.91	3	185.67	GPRUS	Female
2019	6	Elizabet Tursynbayeva	KZ	61.73	4	118.72	6	180.45	GPRUS	Female
2019	10	Brendan Kerry	AU	65.22	10	132.37	9	197.59	GPRUS	Male
2019	11	Artur Dmitriev	RU	67.58	9	122.00	11	189.58	GPRUS	Male
2019	7	Andrei Lazukin	RU	62.45	11	153.33	5	215.78	GPRUS	Male
2019	1	Alina Zagitova	RU	80.78	1	142.17	1	222.95	GPRUS	Female
2019	4	Alexia Paganini	CH	63.43	3	119.07	5	182.50	GPRUS	Female
2019	8	Alexei Krasnozhon	US	75.32	6	132.69	8	208.01	GPRUS	Male
2019	9	Alexander Majorov	SE	82.33	3	123.26	10	205.59	GPRUS	Male
2019	5	Vincent Zhou	US	76.38	6	149.37	3	225.75	GPUSA	Male
2019	10	Starr Andrews	US	56.03	9	94.53	10	150.56	GPUSA	Female
2019	3	Sofia Samodurova	RU	64.41	3	134.29	3	198.70	GPUSA	Female
2019	3	Sergei Voronov	RU	78.18	4	148.26	4	226.44	GPUSA	Male
2019	1	Satoko Miyahara	JP	73.86	1	145.85	1	219.71	GPUSA	Female
2019	10	Romain Ponsart	FR	71.48	8	116.44	11	187.92	GPUSA	Male
2019	7	Polina Tsurskaya	RU	58.42	8	101.03	8	159.45	GPUSA	Female
2019	1	Nathan Chen	US	90.58	1	189.99	1	280.57	GPUSA	Male
2019	6	Nam Nguyen	CA	69.86	9	143.13	6	212.99	GPUSA	Male
2019	8	Morisi Kvitelashvili	GE	68.58	11	136.54	7	205.12	GPUSA	Male
2019	2	Michal Březina	CZ	82.09	2	157.42	2	239.51	GPUSA	Male
2019	6	Megan Wessenberg	US	60.20	6	110.13	6	170.33	GPUSA	Female
2019	4	Matteo Rizzo	IT	78.09	5	147.72	5	225.81	GPUSA	Male
2019	8	Marin Honda	JP	62.74	4	95.30	9	158.04	GPUSA	Female
2019	NA	Loena Hendrickx	BE	54.13	10	NA	NA	NA	GPUSA	Female
2019	5	Laurine Lecavelier	FR	59.57	7	112.84	5	172.41	GPUSA	Female
2019	11	Kevin Reynolds	CA	61.62	12	124.01	10	185.63	GPUSA	Male
2019	2	Kaori Sakamoto	JP	71.29	2	142.61	2	213.90	GPUSA	Female
2019	7	Julian Zhi-Jie Yee	MY	81.52	3	125.99	9	207.51	GPUSA	Male
2019	12	Jimmy Ma	US	71.53	7	113.53	12	185.06	GPUSA	Male
2019	4	Bradie Tennell	US	61.72	5	131.17	4	192.89	GPUSA	Female
2019	9	Alexei Bychenko	IL	69.69	10	127.78	8	197.47	GPUSA	Male
2019	9	Alaine Chartrand	CA	46.99	11	108.50	7	155.49	GPUSA	Female
2019	5	Stanislava Konstantinova	RU	54.91	10	134.76	4	189.67	GPFRA	Female
2019	1	Rika Kihira	JP	67.64	2	138.28	1	205.92	GPFRA	Female
2019	12	Matilda Algotsson	SE	48.58	12	97.77	11	146.35	GPFRA	Female
2019	6	Marin Honda	JP	65.37	4	123.24	6	188.61	GPFRA	Female
2019	7	Maria Sotskova	RU	61.76	5	115.83	7	177.59	GPFRA	Female
2019	2	Mai Mihara	JP	67.95	1	134.86	3	202.81	GPFRA	Female
2019	8	Maé-Bérénice Méité	FR	60.86	7	107.16	8	168.02	GPFRA	Female
2019	11	Léa Serna	FR	55.31	9	94.18	12	149.49	GPFRA	Female
2019	9	Laurine Lecavelier	FR	51.66	11	105.58	9	157.24	GPFRA	Female
2019	4	Evgenia Medvedeva	RU	67.55	3	125.26	5	192.81	GPFRA	Female
2019	3	Bradie Tennell	US	61.34	6	136.44	2	197.78	GPFRA	Female
2019	10	Alexia Paganini	CH	56.88	8	99.63	10	156.51	GPFRA	Female
2018	1	Kaori Sakamoto	JP	71.34	2	142.87	1	214.21	4CC	Female
2018	2	Mai Mihara	JP	69.84	3	140.73	2	210.57	4CC	Female
2018	3	Satoko Miyahara	JP	71.74	1	135.28	3	207.02	4CC	Female
2018	4	Dabin Choi	KR	62.30	5	127.93	4	190.23	4CC	Female
2018	5	Mariah Bell	US	62.90	4	122.94	5	185.84	4CC	Female
2018	6	Hanul Kim	KR	61.15	6	111.95	8	173.10	4CC	Female
2018	7	Starr Andrews	US	60.61	7	112.04	7	172.65	4CC	Female
2018	8	Alaine Chartrand	CA	59.86	8	112.55	6	172.41	4CC	Female
2018	9	Angela Wang	US	58.97	9	102.07	11	161.04	4CC	Female
2018	10	Xiangning Li	CN	57.01	10	103.39	10	160.40	4CC	Female
2018	11	Soyoun Park	KR	53.05	12	106.43	9	159.48	4CC	Female
2018	12	Elizabet Tursynbayeva	KZ	56.52	11	99.67	13	156.19	4CC	Female
2018	13	Alicia Pineault	CA	51.53	14	101.28	12	152.81	4CC	Female
2018	14	Brooklee Han	AU	52.29	13	98.36	14	150.65	4CC	Female
2018	15	Michelle Long	CA	49.77	17	97.73	15	147.50	4CC	Female
2018	16	Kailani Craine	AU	50.79	16	90.25	16	141.04	4CC	Female
2018	17	Ziquan Zhao	CN	48.78	18	90.23	17	139.01	4CC	Female
2018	18	Amy Lin	TW	51.14	15	86.26	18	137.40	4CC	Female
2018	19	Chloe Ing	SG	45.30	19	84.40	20	129.70	4CC	Female
2018	20	Aiza Imambek	KZ	36.47	20	84.53	19	121.00	4CC	Female
2018	21	Joanna So	HK	35.51	21	72.18	21	107.69	4CC	Female
2018	22	Natalie Pailin Sangkagalo	TH	33.50	22	60.11	22	93.61	4CC	Female
2018	23	Thita Lamsam	TH	28.26	23	51.07	23	79.33	4CC	Female
2018	1	Boyang Jin	CN	100.17	2	200.78	1	300.95	4CC	Male
2018	2	Shoma Uno	JP	100.49	1	197.45	2	297.94	4CC	Male
2018	3	Jason Brown	US	89.78	4	179.44	3	269.22	4CC	Male
2018	4	Keiji Tanaka	JP	90.68	3	169.63	5	260.31	4CC	Male
2018	5	Max Aaron	US	84.15	6	171.30	4	255.45	4CC	Male
2018	6	Misha Ge	UZ	82.27	8	166.69	7	248.96	4CC	Male
2018	7	Kevin Reynolds	CA	74.65	13	166.85	6	241.50	4CC	Male
2018	8	Elladj Baldé	CA	75.17	12	163.03	8	238.20	4CC	Male
2018	9	Nam Nguyen	CA	84.09	7	153.43	10	237.52	4CC	Male
2018	10	Han Yan	CN	84.74	5	143.19	12	227.93	4CC	Male
2018	11	Grant Hochstein	US	70.80	15	155.59	9	226.39	4CC	Male
2018	12	Takahito Mura	JP	76.66	10	148.75	11	225.41	4CC	Male
2018	13	Brendan Kerry	AU	79.57	9	140.38	14	219.95	4CC	Male
2018	14	Junehyoung Lee	KR	69.93	16	141.93	13	211.86	4CC	Male
2018	15	Denis Ten	KZ	75.30	11	135.52	15	210.82	4CC	Male
2018	16	Julian Zhi-Jie Yee	MY	68.45	17	129.23	16	197.68	4CC	Male
2018	17	Chih-I Tsao	TW	72.57	14	122.64	19	195.21	4CC	Male
2018	18	Donovan Carrillo	MX	59.07	22	126.84	17	185.91	4CC	Male
2018	19	He Zhang	CN	63.62	19	121.20	20	184.82	4CC	Male
2018	20	Geonhyeong An	KR	56.67	23	123.59	18	180.26	4CC	Male
2018	21	Andrew Dodds	AU	63.69	18	114.12	23	177.81	4CC	Male
2018	22	Sihyeong Lee	KR	62.65	20	114.42	22	177.07	4CC	Male
2018	23	Abzal Rakimgaliev	KZ	60.77	21	114.81	21	175.58	4CC	Male
2018	24	Leslie Man Cheuk Ip	HK	53.80	24	96.43	24	150.23	4CC	Male
2018	25	Micah Kai Lynette	TH	53.29	25	NA	NA	NA	4CC	Male
2018	26	Harrison Jon Yen Wong	HK	52.78	26	NA	NA	NA	4CC	Male
2018	27	Kai Xiang Chew	MY	50.92	27	NA	NA	NA	4CC	Male
2018	28	Mark Webster	AU	49.45	28	NA	NA	NA	4CC	Male
2018	29	Harry Hau Yin Lee	HK	43.98	29	NA	NA	NA	4CC	Male
2018	30	Micah Tang	TW	43.05	30	NA	NA	NA	4CC	Male
2018	1	Alina Zagitova	RU	80.27	1	157.97	1	238.24	EC	Female
2018	2	Evgenia Medvedeva	RU	78.57	2	154.29	2	232.86	EC	Female
2018	3	Carolina Kostner	IT	78.30	3	125.95	4	204.25	EC	Female
2018	4	Maria Sotskova	RU	68.70	4	132.11	3	200.81	EC	Female
2018	5	Loena Hendrickx	BE	55.13	8	121.78	5	176.91	EC	Female
2018	6	Nicole Rajičová	SK	61.01	5	110.89	6	171.90	EC	Female
2018	7	Alexia Paganini	CH	54.95	9	106.67	9	161.62	EC	Female
2018	8	Maé-Bérénice Méité	FR	54.14	10	105.56	10	159.70	EC	Female
2018	9	Emmi Peltonen	FI	52.68	11	106.80	8	159.48	EC	Female
2018	10	Nicole Schott	DE	48.37	18	109.47	7	157.84	EC	Female
2018	11	Laurine Lecavelier	FR	55.36	7	98.75	12	154.11	EC	Female
2018	12	Eliska Brezinova	CZ	52.06	12	97.63	14	149.69	EC	Female
2018	13	Ivett Tóth	HU	50.70	15	98.28	13	148.98	EC	Female
2018	14	Viveca Lindfors	FI	51.62	14	96.27	17	147.89	EC	Female
2018	15	Micol Cristini	IT	48.22	19	99.58	11	147.80	EC	Female
2018	16	Lea Johanna Dastich	DE	49.89	16	96.93	15	146.82	EC	Female
2018	17	Anita Östlund	SE	56.04	6	89.10	20	145.14	EC	Female
2018	18	Anne Line Gjersem	NO	48.70	17	93.98	18	142.68	EC	Female
2018	19	Giada Russo	IT	45.81	23	96.57	16	142.38	EC	Female
2018	20	Daša Grm	SI	47.40	20	89.91	19	137.31	EC	Female
2018	21	Pernille Sørensen	DK	45.76	24	88.18	21	133.94	EC	Female
2018	22	Elzbieta Kropa	LT	46.06	21	87.81	22	133.87	EC	Female
2018	23	Anna Khnychenkova	UA	51.84	13	80.86	23	132.70	EC	Female
2018	24	Silvia Hugec	SK	45.98	22	77.47	24	123.45	EC	Female
2018	25	Kristina Lisovskaja	EE	45.74	25	NA	NA	NA	EC	Female
2018	26	Kyarha Van Tiel	NL	45.28	26	NA	NA	NA	EC	Female
2018	27	Natasha Mckay	GB	45.12	27	NA	NA	NA	EC	Female
2018	28	Fruzsina Medgyesi	HU	44.71	28	NA	NA	NA	EC	Female
2018	29	Julia Sauter	RO	44.57	29	NA	NA	NA	EC	Female
2018	30	Natalie Klotz	AT	43.53	30	NA	NA	NA	EC	Female
2018	31	Matilda Algotsson	SE	43.28	31	NA	NA	NA	EC	Female
2018	32	Sila Saygi	TR	43.05	32	NA	NA	NA	EC	Female
2018	33	Valentina Matos	ES	39.66	33	NA	NA	NA	EC	Female
2018	34	Elzbieta Gabryszak	PL	38.00	34	NA	NA	NA	EC	Female
2018	35	Kim Cheremsky	AZ	37.61	35	NA	NA	NA	EC	Female
2018	36	Diana Nikitina	LV	36.71	36	NA	NA	NA	EC	Female
2018	37	Antonina Dubinina	RS	36.69	37	NA	NA	NA	EC	Female
2018	38	Aimee Buchanan	IL	33.87	38	NA	NA	NA	EC	Female
2018	39	Presiyana Dimitrova	BG	24.76	39	NA	NA	NA	EC	Female
2018	1	Javier Fernández	ES	103.82	1	191.73	1	295.55	EC	Male
2018	2	Dmitri Aliev	RU	91.33	2	182.73	2	274.06	EC	Male
2018	3	Mikhail Kolyada	RU	83.41	4	175.49	3	258.90	EC	Male
2018	4	Deniss Vasiljevs	LV	85.11	3	158.41	5	243.52	EC	Male
2018	5	Alexei Bychenko	IL	74.97	8	163.47	4	238.44	EC	Male
2018	6	Alexander Samarin	RU	74.25	9	155.56	6	229.81	EC	Male
2018	7	Alexander Majorov	SE	71.28	12	154.58	7	225.86	EC	Male
2018	8	Michal Březina	CZ	72.72	10	152.48	8	225.20	EC	Male
2018	9	Matteo Rizzo	IT	78.26	6	141.17	9	219.43	EC	Male
2018	10	Jorik Hendrickx	BE	78.56	5	139.61	12	218.17	EC	Male
2018	11	Chafik Besseghier	FR	70.35	13	140.82	10	211.17	EC	Male
2018	12	Morisi Kvitelashvili	GE	76.74	7	133.73	14	210.47	EC	Male
2018	13	Phillip Harris	GB	67.77	15	140.45	11	208.22	EC	Male
2018	14	Romain Ponsart	FR	61.45	20	139.27	13	200.72	EC	Male
2018	15	Slavik Hayrapetyan	AM	69.49	14	127.14	16	196.63	EC	Male
2018	16	Paul Fentz	DE	72.54	11	122.43	17	194.97	EC	Male
2018	17	Irakli Maysuradze	GE	63.69	18	127.53	15	191.22	EC	Male
2018	18	Stéphane Walker	CH	65.96	16	119.45	20	185.41	EC	Male
2018	19	Valtter Virtanen	FI	60.23	24	121.54	18	181.77	EC	Male
2018	20	Felipe Montoya Pulgarín	ES	61.23	22	120.49	19	181.72	EC	Male
2018	21	Daniel Albert Naurits	EE	60.76	23	115.34	21	176.10	EC	Male
2018	22	Sondre Oddvoll Bøe	NO	61.85	19	108.79	22	170.64	EC	Male
2018	23	Burak Demirboga	TR	61.27	21	105.95	23	167.22	EC	Male
2018	24	Ihor Reznichenko	PL	63.96	17	101.69	24	165.65	EC	Male
2018	25	Yaroslav Paniot	UA	60.07	25	NA	NA	NA	EC	Male
2018	26	Daniel Samohin	IL	59.18	26	NA	NA	NA	EC	Male
2018	27	Nicholas Vrdoljak	HR	58.30	27	NA	NA	NA	EC	Male
2018	28	Jiri Belohradsky	CZ	58.30	28	NA	NA	NA	EC	Male
2018	29	Michael Neuman	SK	57.44	29	NA	NA	NA	EC	Male
2018	30	Thomas Kennes	NL	54.65	30	NA	NA	NA	EC	Male
2018	31	Davidé Lewton Brain	MC	54.64	31	NA	NA	NA	EC	Male
2018	32	Larry Loupolover	AZ	52.44	32	NA	NA	NA	EC	Male
2018	33	Alexander Maszljanko	HU	52.01	33	NA	NA	NA	EC	Male
2018	34	Nicky-Leo Obreykov	BG	50.24	34	NA	NA	NA	EC	Male
2018	35	Yakau Zenko	BY	47.26	35	NA	NA	NA	EC	Male
2018	36	Conor Stakelum	IE	43.05	36	NA	NA	NA	EC	Male
2018	1	Kaetlyn Osmond	CA	76.06	1	136.85	1	212.91	GPCAN	Female
2018	2	Maria Sotskova	RU	66.10	3	126.42	2	192.52	GPCAN	Female
2018	3	Ashley Wagner	US	61.57	7	122.37	4	183.94	GPCAN	Female
2018	4	Courtney Hicks	US	64.06	4	118.51	5	182.57	GPCAN	Female
2018	5	Marin Honda	JP	52.60	10	125.64	3	178.24	GPCAN	Female
2018	6	Rika Hongo	JP	61.60	6	114.74	6	176.34	GPCAN	Female
2018	7	Karen Chen	US	61.77	5	108.63	7	170.40	GPCAN	Female
2018	8	Laurine Lecavelier	FR	59.08	8	107.35	8	166.43	GPCAN	Female
2018	9	Anna Pogorilaya	RU	69.05	2	87.84	10	156.89	GPCAN	Female
2018	10	Kailani Craine	AU	54.96	9	88.07	9	143.03	GPCAN	Female
2018	11	Alaine Chartrand	CA	46.51	11	87.66	11	134.17	GPCAN	Female
2018	12	Larkyn Austman	CA	41.79	12	81.77	12	123.56	GPCAN	Female
2018	1	Shoma Uno	JP	103.62	1	197.48	1	301.10	GPCAN	Male
2018	2	Jason Brown	US	90.71	3	170.43	2	261.14	GPCAN	Male
2018	3	Alexander Samarin	RU	84.02	4	166.04	3	250.06	GPCAN	Male
2018	4	Patrick Chan	CA	94.43	2	151.27	7	245.70	GPCAN	Male
2018	5	Jorik Hendrickx	BE	82.08	6	155.23	5	237.31	GPCAN	Male
2018	6	Michal Březina	CZ	80.34	7	156.70	4	237.04	GPCAN	Male
2018	7	Nicolas Nadeau	CA	74.23	9	155.20	6	229.43	GPCAN	Male
2018	8	Keegan Messing	CA	82.17	5	135.58	10	217.75	GPCAN	Male
2018	9	Junhwan Cha	KR	68.46	11	141.86	8	210.32	GPCAN	Male
2018	10	Paul Fentz	DE	68.48	10	133.12	11	201.60	GPCAN	Male
2018	11	Brendan Kerry	AU	63.19	12	138.37	9	201.56	GPCAN	Male
2018	12	Takahito Mura	JP	74.82	8	111.84	12	186.66	GPCAN	Male
2018	1	Alina Zagitova	RU	69.44	4	144.44	1	213.88	GPCHN	Female
2018	2	Wakaba Higuchi	JP	70.53	2	141.99	2	212.52	GPCHN	Female
2018	3	Elena Radionova	RU	70.48	3	136.34	4	206.82	GPCHN	Female
2018	4	Mai Mihara	JP	66.90	7	139.17	3	206.07	GPCHN	Female
2018	5	Marin Honda	JP	66.90	6	131.42	5	198.32	GPCHN	Female
2018	6	Gabrielle Daleman	CA	70.65	1	126.18	7	196.83	GPCHN	Female
2018	7	Elizaveta Tuktamysheva	RU	67.10	5	129.58	6	196.68	GPCHN	Female
2018	8	Xiangning Li	CN	59.20	8	115.62	8	174.82	GPCHN	Female
2018	9	Dabin Choi	KR	53.90	9	112.09	9	165.99	GPCHN	Female
2018	10	Amber Glenn	US	52.61	10	98.53	10	151.14	GPCHN	Female
2018	11	Ziquan Zhao	CN	50.39	11	94.32	11	144.71	GPCHN	Female
2018	1	Mikhail Kolyada	RU	103.13	1	176.25	3	279.38	GPCHN	Male
2018	2	Boyang Jin	CN	93.89	2	170.59	5	264.48	GPCHN	Male
2018	3	Max Aaron	US	83.11	5	176.58	1	259.69	GPCHN	Male
2018	4	Vincent Zhou	US	80.23	8	176.43	2	256.66	GPCHN	Male
2018	5	Han Yan	CN	82.22	6	172.39	4	254.61	GPCHN	Male
2018	6	Javier Fernández	ES	90.57	3	162.49	6	253.06	GPCHN	Male
2018	7	Keiji Tanaka	JP	87.19	4	159.98	8	247.17	GPCHN	Male
2018	8	Kevin Reynolds	CA	64.40	10	162.10	7	226.50	GPCHN	Male
2018	9	Grant Hochstein	US	80.55	7	135.89	9	216.44	GPCHN	Male
2018	10	Alexander Majorov	SE	64.27	11	121.77	10	186.04	GPCHN	Male
2018	11	Alexander Petrov	RU	68.58	9	117.44	12	186.02	GPCHN	Male
2018	12	He Zhang	CN	46.99	12	120.59	11	167.58	GPCHN	Male
2018	1	Alina Zagitova	RU	76.27	2	147.03	1	223.30	GPF	Female
2018	2	Maria Sotskova	RU	74.00	4	142.28	2	216.28	GPF	Female
2018	3	Kaetlyn Osmond	CA	77.04	1	138.12	5	215.16	GPF	Female
2018	4	Carolina Kostner	IT	72.82	6	141.83	3	214.65	GPF	Female
2018	5	Satoko Miyahara	JP	74.61	3	138.88	4	213.49	GPF	Female
2018	6	Wakaba Higuchi	JP	73.26	5	128.85	6	202.11	GPF	Female
2018	1	Nathan Chen	US	103.32	1	183.19	2	286.51	GPF	Male
2018	2	Shoma Uno	JP	101.51	2	184.50	1	286.01	GPF	Male
2018	3	Mikhail Kolyada	RU	99.22	3	182.78	3	282.00	GPF	Male
2018	4	Sergei Voronov	RU	87.77	5	178.82	4	266.59	GPF	Male
2018	5	Adam Rippon	US	86.19	6	168.14	5	254.33	GPF	Male
2018	6	Jason Brown	US	89.02	4	164.79	6	253.81	GPF	Male
2018	1	Alina Zagitova	RU	62.46	5	151.34	1	213.80	GPFRA	Female
2018	2	Maria Sotskova	RU	67.79	2	140.99	2	208.78	GPFRA	Female
2018	3	Kaetlyn Osmond	CA	69.05	1	137.72	4	206.77	GPFRA	Female
2018	4	Mai Mihara	JP	64.57	4	137.55	5	202.12	GPFRA	Female
2018	5	Elizabet Tursynbayeva	KZ	62.29	6	138.69	3	200.98	GPFRA	Female
2018	6	Yuna Shiraiwa	JP	66.05	3	127.13	6	193.18	GPFRA	Female
2018	7	Nicole Schott	DE	55.54	10	116.85	7	172.39	GPFRA	Female
2018	8	Maé-Bérénice Méité	FR	58.96	8	112.44	9	171.40	GPFRA	Female
2018	9	Elizaveta Tuktamysheva	RU	53.03	11	114.62	8	167.65	GPFRA	Female
2018	10	Polina Edmunds	US	56.31	9	101.46	10	157.77	GPFRA	Female
2018	11	Laurine Lecavelier	FR	60.68	7	93.67	11	154.35	GPFRA	Female
2018	1	Javier Fernández	ES	107.86	1	175.85	2	283.71	GPFRA	Male
2018	2	Shoma Uno	JP	93.92	2	179.40	1	273.32	GPFRA	Male
2018	3	Misha Ge	UZ	85.41	6	172.93	3	258.34	GPFRA	Male
2018	4	Alexander Samarin	RU	91.51	3	161.62	4	253.13	GPFRA	Male
2018	5	Alexei Bychenko	IL	86.79	5	160.65	5	247.44	GPFRA	Male
2018	6	Morisi Kvitelashvili	GE	86.98	4	153.52	8	240.50	GPFRA	Male
2018	7	Max Aaron	US	78.64	8	158.56	6	237.20	GPFRA	Male
2018	8	Denis Ten	KZ	83.70	7	144.87	10	228.57	GPFRA	Male
2018	9	Vincent Zhou	US	66.12	10	156.09	7	222.21	GPFRA	Male
2018	10	Kévin Aymoz	FR	70.00	9	150.43	9	220.43	GPFRA	Male
2018	11	Romain Ponsart	FR	63.81	11	134.31	11	198.12	GPFRA	Male
2018	1	Evgenia Medvedeva	RU	79.99	1	144.40	1	224.39	GPJPN	Female
2018	2	Carolina Kostner	IT	74.57	2	137.67	3	212.24	GPJPN	Female
2018	3	Polina Tsurskaya	RU	70.04	3	140.15	2	210.19	GPJPN	Female
2018	4	Mirai Nagasu	US	65.17	5	129.29	4	194.46	GPJPN	Female
2018	5	Satoko Miyahara	JP	65.05	6	126.75	6	191.80	GPJPN	Female
2018	6	Alena Leonova	RU	63.61	7	127.34	5	190.95	GPJPN	Female
2018	7	Rika Hongo	JP	65.83	4	122.00	7	187.83	GPJPN	Female
2018	8	Yuna Shiraiwa	JP	57.34	8	114.60	8	171.94	GPJPN	Female
2018	9	Mariah Bell	US	57.25	9	108.79	10	166.04	GPJPN	Female
2018	10	Nicole Rajičová	SK	53.36	10	106.42	11	159.78	GPJPN	Female
2018	11	Alaine Chartrand	CA	49.60	12	109.76	9	159.36	GPJPN	Female
2018	12	Soyoun Park	KR	51.54	11	84.25	12	135.79	GPJPN	Female
2018	1	Sergei Voronov	RU	90.06	1	181.06	1	271.12	GPJPN	Male
2018	2	Adam Rippon	US	84.95	4	177.04	2	261.99	GPJPN	Male
2018	3	Alexei Bychenko	IL	85.52	2	166.55	3	252.07	GPJPN	Male
2018	4	Jason Brown	US	85.36	3	160.59	4	245.95	GPJPN	Male
2018	5	Keegan Messing	CA	80.13	5	155.67	6	235.80	GPJPN	Male
2018	6	Deniss Vasiljevs	LV	76.51	8	158.29	5	234.80	GPJPN	Male
2018	7	Kazuki Tomono	JP	79.88	6	152.05	7	231.93	GPJPN	Male
2018	8	Dmitri Aliev	RU	77.51	7	145.94	9	223.45	GPJPN	Male
2018	9	Michal Březina	CZ	76.24	9	144.21	10	220.45	GPJPN	Male
2018	10	Nam Nguyen	CA	65.82	11	148.69	8	214.51	GPJPN	Male
2018	11	Hiroaki Sato	JP	75.95	10	123.25	11	199.20	GPJPN	Male
2018	1	Evgenia Medvedeva	RU	80.75	1	150.46	1	231.21	GPRUS	Female
2018	2	Carolina Kostner	IT	74.62	2	141.36	2	215.98	GPRUS	Female
2018	3	Wakaba Higuchi	JP	69.60	3	137.57	3	207.17	GPRUS	Female
2018	4	Elena Radionova	RU	68.75	5	126.77	4	195.52	GPRUS	Female
2018	5	Kaori Sakamoto	JP	68.88	4	125.12	5	194.00	GPRUS	Female
2018	6	Mariah Bell	US	63.85	7	124.71	6	188.56	GPRUS	Female
2018	7	Valeria Mikhailova	RU	63.38	8	121.71	8	185.09	GPRUS	Female
2018	8	Elizabet Tursynbayeva	KZ	63.92	6	121.03	9	184.95	GPRUS	Female
2018	9	Mirai Nagasu	US	56.15	9	122.10	7	178.25	GPRUS	Female
2018	10	Nicole Schott	DE	55.55	10	113.17	10	168.72	GPRUS	Female
2018	11	Maé-Bérénice Méité	FR	54.24	11	106.72	12	160.96	GPRUS	Female
2018	12	Anastasiya Galustyan	AM	48.10	12	106.80	11	154.90	GPRUS	Female
2018	1	Nathan Chen	US	100.54	1	193.25	2	293.79	GPRUS	Male
2018	2	Yuzuru Hanyu	JP	94.85	2	195.92	1	290.77	GPRUS	Male
2018	3	Mikhail Kolyada	RU	85.79	4	185.27	3	271.06	GPRUS	Male
2018	4	Misha Ge	UZ	85.02	5	170.31	4	255.33	GPRUS	Male
2018	5	Morisi Kvitelashvili	GE	80.67	8	169.59	5	250.26	GPRUS	Male
2018	6	Dmitri Aliev	RU	88.77	3	150.84	7	239.61	GPRUS	Male
2018	7	Nam Nguyen	CA	80.74	7	157.71	6	238.45	GPRUS	Male
2018	8	Deniss Vasiljevs	LV	82.44	6	145.09	9	227.53	GPRUS	Male
2018	9	Denis Ten	KZ	69.00	10	145.35	8	214.35	GPRUS	Male
2018	10	Andrei Lazukin	RU	78.54	9	133.60	11	212.14	GPRUS	Male
2018	11	Grant Hochstein	US	67.56	11	138.53	10	206.09	GPRUS	Male
2018	12	Daniel Samohin	IL	62.02	12	121.77	12	183.79	GPRUS	Male
2018	1	Satoko Miyahara	JP	70.72	1	143.31	1	214.03	GPUSA	Female
2018	2	Kaori Sakamoto	JP	69.40	2	141.19	2	210.59	GPUSA	Female
2018	3	Bradie Tennell	US	67.01	4	137.09	3	204.10	GPUSA	Female
2018	4	Polina Tsurskaya	RU	63.20	8	132.36	4	195.56	GPUSA	Female
2018	5	Serafima Sakhanovich	RU	66.28	5	123.47	5	189.75	GPUSA	Female
2018	6	Gabrielle Daleman	CA	68.08	3	121.06	8	189.14	GPUSA	Female
2018	7	Alena Leonova	RU	63.91	7	122.02	7	185.93	GPUSA	Female
2018	8	Karen Chen	US	59.53	9	123.27	6	182.80	GPUSA	Female
2018	9	Nicole Rajičová	SK	55.43	10	112.18	9	167.61	GPUSA	Female
2018	10	Xiangning Li	CN	55.24	11	109.08	10	164.32	GPUSA	Female
2018	NA	Ashley Wagner	US	64.12	6	NA	NA	NA	GPUSA	Female
2018	1	Nathan Chen	US	104.12	1	171.76	2	275.88	GPUSA	Male
2018	2	Adam Rippon	US	89.04	2	177.41	1	266.45	GPUSA	Male
2018	3	Sergei Voronov	RU	87.51	3	169.98	3	257.49	GPUSA	Male
2018	4	Boyang Jin	CN	77.97	6	168.06	4	246.03	GPUSA	Male
2018	5	Han Yan	CN	85.97	4	142.36	7	228.33	GPUSA	Male
2018	6	Ross Miner	US	71.59	8	148.03	5	219.62	GPUSA	Male
2018	7	Takahito Mura	JP	75.05	7	137.72	8	212.77	GPUSA	Male
2018	8	Liam Firus	CA	65.17	11	145.66	6	210.83	GPUSA	Male
2018	9	Kevin Reynolds	CA	69.10	10	134.95	9	204.05	GPUSA	Male
2018	10	Roman Sadovsky	CA	70.85	9	129.25	10	200.10	GPUSA	Male
2018	NA	Daniel Samohin	IL	82.28	5	NA	NA	NA	GPUSA	Male
2018	NA	Maxim Kovtun	RU	64.98	12	NA	NA	NA	GPUSA	Male
2018	1	Alina Zagitova	RU	82.92	1	156.65	2	239.57	OLY	Female
2018	2	Evgenia Medvedeva	RU	81.61	2	156.65	1	238.26	OLY	Female
2018	3	Kaetlyn Osmond	CA	78.87	3	152.15	3	231.02	OLY	Female
2018	4	Satoko Miyahara	JP	75.94	4	146.44	4	222.38	OLY	Female
2018	5	Carolina Kostner	IT	73.15	6	139.29	5	212.44	OLY	Female
2018	6	Kaori Sakamoto	JP	73.18	5	136.53	6	209.71	OLY	Female
2018	7	Dabin Choi	KR	67.77	8	131.49	8	199.26	OLY	Female
2018	8	Maria Sotskova	RU	63.86	12	134.24	7	198.10	OLY	Female
2018	9	Bradie Tennell	US	64.01	11	128.34	9	192.35	OLY	Female
2018	10	Mirai Nagasu	US	66.93	9	119.61	12	186.54	OLY	Female
2018	11	Karen Chen	US	65.90	10	119.75	11	185.65	OLY	Female
2018	12	Elizabet Tursynbayeva	KZ	58.82	15	118.30	13	177.12	OLY	Female
2018	13	Hanul Kim	KR	54.33	21	121.38	10	175.71	OLY	Female
2018	14	Nicole Rajičová	SK	60.59	13	114.60	15	175.19	OLY	Female
2018	15	Gabrielle Daleman	CA	68.90	7	103.56	19	172.46	OLY	Female
2018	16	Loena Hendrickx	BE	55.16	20	116.72	14	171.88	OLY	Female
2018	17	Kailani Craine	AU	56.77	16	111.84	16	168.61	OLY	Female
2018	18	Nicole Schott	DE	59.20	14	109.26	17	168.46	OLY	Female
2018	19	Maé-Bérénice Méité	FR	53.67	22	106.25	18	159.92	OLY	Female
2018	20	Emmi Peltonen	FI	55.28	18	101.86	21	157.14	OLY	Female
2018	21	Alexia Paganini	CH	55.26	19	101.00	22	156.26	OLY	Female
2018	22	Xiangning Li	CN	52.46	24	101.97	20	154.43	OLY	Female
2018	23	Ivett Tóth	HU	53.22	23	97.21	23	150.43	OLY	Female
2018	24	Isadora Williams	BR	55.74	17	88.44	24	144.18	OLY	Female
2018	25	Larkyn Austman	CA	51.42	25	NA	NA	NA	OLY	Female
2018	26	Diana Nikitina	LV	51.12	26	NA	NA	NA	OLY	Female
2018	27	Giada Russo	IT	50.88	27	NA	NA	NA	OLY	Female
2018	28	Anita Östlund	SE	49.14	28	NA	NA	NA	OLY	Female
2018	29	Anna Khnychenkova	UA	47.59	29	NA	NA	NA	OLY	Female
2018	30	Aiza Imambek	KZ	44.40	30	NA	NA	NA	OLY	Female
2018	1	Yuzuru Hanyu	JP	111.68	1	206.17	2	317.85	OLY	Male
2018	2	Shoma Uno	JP	104.17	3	202.73	3	306.90	OLY	Male
2018	3	Javier Fernández	ES	107.58	2	197.66	4	305.24	OLY	Male
2018	4	Boyang Jin	CN	103.32	4	194.45	5	297.77	OLY	Male
2018	5	Nathan Chen	US	82.27	17	215.08	1	297.35	OLY	Male
2018	6	Vincent Zhou	US	84.53	12	192.16	6	276.69	OLY	Male
2018	7	Dmitri Aliev	RU	98.98	5	168.53	13	267.51	OLY	Male
2018	8	Mikhail Kolyada	RU	86.69	8	177.56	7	264.25	OLY	Male
2018	9	Patrick Chan	CA	90.01	6	173.42	8	263.43	OLY	Male
2018	10	Adam Rippon	US	87.95	7	171.41	10	259.36	OLY	Male
2018	11	Alexei Bychenko	IL	84.13	13	172.88	9	257.01	OLY	Male
2018	12	Keegan Messing	CA	85.11	10	170.32	12	255.43	OLY	Male
2018	13	Daniel Samohin	IL	80.69	18	170.75	11	251.44	OLY	Male
2018	14	Jorik Hendrickx	BE	84.74	11	164.21	16	248.95	OLY	Male
2018	15	Junhwan Cha	KR	83.43	15	165.16	14	248.59	OLY	Male
2018	16	Michal Březina	CZ	85.15	9	160.92	18	246.07	OLY	Male
2018	17	Misha Ge	UZ	83.90	14	161.04	17	244.94	OLY	Male
2018	18	Keiji Tanaka	JP	80.05	20	164.78	15	244.83	OLY	Male
2018	19	Deniss Vasiljevs	LV	79.52	21	155.06	20	234.58	OLY	Male
2018	20	Brendan Kerry	AU	83.06	16	150.75	21	233.81	OLY	Male
2018	21	Matteo Rizzo	IT	75.63	23	156.78	19	232.41	OLY	Male
2018	22	Paul Fentz	DE	74.73	24	139.82	22	214.55	OLY	Male
2018	23	Han Yan	CN	80.63	19	132.38	23	213.01	OLY	Male
2018	24	Morisi Kvitelashvili	GE	76.56	22	128.01	24	204.57	OLY	Male
2018	25	Julian Zhi-Jie Yee	MY	73.58	25	NA	NA	NA	OLY	Male
2018	26	Chafik Besseghier	FR	72.10	26	NA	NA	NA	OLY	Male
2018	27	Denis Ten	KZ	70.12	27	NA	NA	NA	OLY	Male
2018	28	Michael Christian Martinez	PH	55.56	28	NA	NA	NA	OLY	Male
2018	29	Felipe Montoya Pulgarín	ES	52.41	29	NA	NA	NA	OLY	Male
2018	30	Yaroslav Paniot	UA	46.58	30	NA	NA	NA	OLY	Male
2017	1	Mai Mihara	JP	66.51	4	134.34	1	200.85	4CC	Female
2017	2	Gabrielle Daleman	CA	68.25	1	128.66	3	196.91	4CC	Female
2017	3	Mirai Nagasu	US	62.91	5	132.04	2	194.95	4CC	Female
2017	4	Kaetlyn Osmond	CA	68.21	2	115.96	6	184.17	4CC	Female
2017	5	Dabin Choi	KR	61.62	6	120.79	4	182.41	4CC	Female
2017	6	Mariah Bell	US	61.21	7	115.89	7	177.10	4CC	Female
2017	7	Zijun Li	CN	60.37	8	116.68	5	177.05	4CC	Female
2017	8	Elizabet Tursynbayeva	KZ	66.87	3	109.78	11	176.65	4CC	Female
2017	9	Wakaba Higuchi	JP	58.83	10	113.22	9	172.05	4CC	Female
2017	10	Rika Hongo	JP	59.16	9	108.26	13	167.42	4CC	Female
2017	11	Alaine Chartrand	CA	53.64	14	113.48	8	167.12	4CC	Female
2017	12	Karen Chen	US	55.60	12	111.22	10	166.82	4CC	Female
2017	13	Xiangning Li	CN	55.73	11	109.12	12	164.85	4CC	Female
2017	14	Brooklee Han	AU	48.44	16	103.61	14	152.05	4CC	Female
2017	15	Ziquan Zhao	CN	49.97	15	97.40	15	147.37	4CC	Female
2017	16	Kailani Craine	AU	54.70	13	82.21	17	136.91	4CC	Female
2017	17	Amy Lin	TW	45.40	18	79.62	19	125.02	4CC	Female
2017	18	Maisy Hiu Ching Ma	HK	43.55	19	81.10	18	124.65	4CC	Female
2017	19	Suhhyun Son	KR	38.61	22	83.74	16	122.35	4CC	Female
2017	20	Chloe Ing	SG	41.61	21	78.57	20	120.18	4CC	Female
2017	21	Shuran Yu	SG	43.26	20	75.14	21	118.40	4CC	Female
2017	NA	Nahyun Kim	KR	45.95	17	NA	NA	NA	4CC	Female
2017	NA	Michaela Du Toit	ZA	33.39	23	NA	NA	NA	4CC	Female
2017	1	Nathan Chen	US	103.12	1	204.34	2	307.46	4CC	Male
2017	2	Yuzuru Hanyu	JP	97.04	3	206.67	1	303.71	4CC	Male
2017	3	Shoma Uno	JP	100.28	2	187.77	3	288.05	4CC	Male
2017	4	Patrick Chan	CA	88.46	5	179.52	4	267.98	4CC	Male
2017	5	Boyang Jin	CN	91.33	4	176.18	5	267.51	4CC	Male
2017	6	Jason Brown	US	80.77	9	165.08	6	245.85	4CC	Male
2017	7	Misha Ge	UZ	81.85	8	157.56	8	239.41	4CC	Male
2017	8	Nam Nguyen	CA	72.99	13	164.09	7	237.08	4CC	Male
2017	9	Grant Hochstein	US	81.94	7	153.78	9	235.72	4CC	Male
2017	10	Han Yan	CN	84.08	6	151.37	10	235.45	4CC	Male
2017	11	Brendan Kerry	AU	78.11	10	149.28	11	227.39	4CC	Male
2017	12	Kevin Reynolds	CA	76.36	12	145.95	12	222.31	4CC	Male
2017	13	Keiji Tanaka	JP	77.55	11	142.63	13	220.18	4CC	Male
2017	14	Michael Christian Martinez	PH	72.47	14	141.68	14	214.15	4CC	Male
2017	15	Julian Zhi-Jie Yee	MY	72.21	15	130.46	16	202.67	4CC	Male
2017	16	Sihyeong Lee	KR	65.40	17	130.32	17	195.72	4CC	Male
2017	17	Jinseo Kim	KR	64.26	18	130.79	15	195.05	4CC	Male
2017	18	Junehyoung Lee	KR	67.55	16	120.03	18	187.58	4CC	Male
2017	19	Chih-I Tsao	TW	51.02	22	118.61	19	169.63	4CC	Male
2017	20	Andrew Dodds	AU	60.17	19	101.88	21	162.05	4CC	Male
2017	21	Mark Webster	AU	54.92	20	105.11	20	160.03	4CC	Male
2017	22	Leslie Man Cheuk Ip	HK	52.86	21	93.88	22	146.74	4CC	Male
2017	23	Kai Xiang Chew	MY	47.38	23	91.08	23	138.46	4CC	Male
2017	24	Micah Tang	TW	46.41	24	89.38	24	135.79	4CC	Male
2017	25	Harry Hau Yin Lee	HK	45.27	25	NA	NA	NA	4CC	Male
2017	26	Harrison Jon Yen Wong	HK	45.12	26	NA	NA	NA	4CC	Male
2017	1	Evgenia Medvedeva	RU	78.92	1	150.79	1	229.71	EC	Female
2017	2	Anna Pogorilaya	RU	74.39	2	137.00	3	211.39	EC	Female
2017	3	Carolina Kostner	IT	72.40	3	138.12	2	210.52	EC	Female
2017	4	Maria Sotskova	RU	72.17	4	120.35	5	192.52	EC	Female
2017	5	Laurine Lecavelier	FR	63.81	5	124.29	4	188.10	EC	Female
2017	6	Nicole Rajičová	SK	60.98	7	118.72	6	179.70	EC	Female
2017	7	Loena Hendrickx	BE	55.41	11	117.30	7	172.71	EC	Female
2017	8	Ivett Tóth	HU	61.49	6	111.16	8	172.65	EC	Female
2017	9	Roberta Rodeghiero	IT	57.77	8	103.23	12	161.00	EC	Female
2017	10	Nicole Schott	DE	56.88	9	103.75	10	160.63	EC	Female
2017	11	Emmi Peltonen	FI	53.52	14	107.05	9	160.57	EC	Female
2017	12	Anastasiya Galustyan	AM	56.40	10	98.74	14	155.14	EC	Female
2017	13	Matilda Algotsson	SE	51.35	18	103.28	11	154.63	EC	Female
2017	14	Joshi Helgesson	SE	53.93	13	98.93	13	152.86	EC	Female
2017	15	Helery Hälvin	EE	51.72	16	94.96	15	146.68	EC	Female
2017	16	Maé-Bérénice Méité	FR	54.96	12	90.11	19	145.07	EC	Female
2017	17	Nathalie Weinzierl	DE	48.70	22	94.70	17	143.40	EC	Female
2017	18	Natasha Mckay	GB	45.97	24	94.88	16	140.85	EC	Female
2017	19	Angelina Kuchvalska	LV	49.05	20	90.58	18	139.63	EC	Female
2017	20	Michaela Hanzlikova	CZ	52.39	15	85.84	21	138.23	EC	Female
2017	21	Anna Khnychenkova	UA	48.93	21	87.64	20	136.57	EC	Female
2017	22	Kerstin Frank	AT	51.47	17	80.61	24	132.08	EC	Female
2017	23	Viveca Lindfors	FI	49.48	19	80.62	22	130.10	EC	Female
2017	24	Anne Line Gjersem	NO	48.06	23	80.62	23	128.68	EC	Female
2017	25	Julia Sauter	RO	45.59	25	NA	NA	NA	EC	Female
2017	26	Daša Grm	SI	43.48	26	NA	NA	NA	EC	Female
2017	27	Yasmine Kimiko Yamada	CH	42.33	27	NA	NA	NA	EC	Female
2017	28	Elzbieta Kropa	LT	41.52	28	NA	NA	NA	EC	Female
2017	29	Antonina Dubinina	RS	41.05	29	NA	NA	NA	EC	Female
2017	30	Colette Kaminski	PL	39.83	30	NA	NA	NA	EC	Female
2017	31	Aimee Buchanan	IL	38.49	31	NA	NA	NA	EC	Female
2017	32	Birce Atabey	TR	35.59	32	NA	NA	NA	EC	Female
2017	33	Valentina Matos	ES	34.79	33	NA	NA	NA	EC	Female
2017	34	Hristina Vassileva	BG	24.55	34	NA	NA	NA	EC	Female
2017	1	Javier Fernández	ES	104.25	1	190.59	1	294.84	EC	Male
2017	2	Maxim Kovtun	RU	94.53	2	172.27	2	266.80	EC	Male
2017	3	Mikhail Kolyada	RU	83.96	4	166.22	3	250.18	EC	Male
2017	4	Jorik Hendrickx	BE	82.50	5	160.06	5	242.56	EC	Male
2017	5	Alexei Bychenko	IL	86.68	3	152.56	9	239.24	EC	Male
2017	6	Morisi Kvitelashvili	GE	76.85	10	161.35	4	238.20	EC	Male
2017	7	Deniss Vasiljevs	LV	79.87	6	155.33	6	235.20	EC	Male
2017	8	Alexander Samarin	RU	77.26	9	153.61	7	230.87	EC	Male
2017	9	Chafik Besseghier	FR	76.19	11	151.40	10	227.59	EC	Male
2017	10	Paul Fentz	DE	72.68	12	153.17	8	225.85	EC	Male
2017	11	Alexander Majorov	SE	78.87	7	139.11	12	217.98	EC	Male
2017	12	Michal Březina	CZ	78.61	8	136.91	13	215.52	EC	Male
2017	13	Ivan Righini	IT	69.96	14	140.19	11	210.15	EC	Male
2017	14	Ivan Pavlov	UA	68.94	15	133.93	14	202.87	EC	Male
2017	15	Kévin Aymoz	FR	71.26	13	128.21	18	199.47	EC	Male
2017	16	Graham Newberry	GB	67.79	16	130.27	16	198.06	EC	Male
2017	17	Stéphane Walker	CH	62.86	19	133.88	15	196.74	EC	Male
2017	18	Javier Raya	ES	66.67	17	128.87	17	195.54	EC	Male
2017	19	Maurizio Zandrón	IT	63.79	18	122.61	19	186.40	EC	Male
2017	20	Jiri Belohradsky	CZ	60.99	20	120.63	21	181.62	EC	Male
2017	21	Slavik Hayrapetyan	AM	60.69	21	120.09	22	180.78	EC	Male
2017	22	Daniel Albert Naurits	EE	55.14	24	120.96	20	176.10	EC	Male
2017	23	Valtter Virtanen	FI	56.52	22	107.57	24	164.09	EC	Male
2017	24	Sondre Oddvoll Bøe	NO	55.24	23	107.61	23	162.85	EC	Male
2017	25	Ihor Reznichenko	PL	54.81	25	NA	NA	NA	EC	Male
2017	26	Nicholas Vrdoljak	HR	53.45	26	NA	NA	NA	EC	Male
2017	27	Alexander Borovoj	HU	53.02	27	NA	NA	NA	EC	Male
2017	28	Thomas Kennes	NL	52.95	28	NA	NA	NA	EC	Male
2017	29	Anton Karpuk	BY	52.26	29	NA	NA	NA	EC	Male
2017	30	Mark Gorodnitsky	IL	51.72	30	NA	NA	NA	EC	Male
2017	31	Larry Loupolover	AZ	51.30	31	NA	NA	NA	EC	Male
2017	32	Engin Ali Artan	TR	50.38	32	NA	NA	NA	EC	Male
2017	33	Daniel Samohin	IL	50.33	33	NA	NA	NA	EC	Male
2017	34	Michael Neuman	SK	47.67	34	NA	NA	NA	EC	Male
2017	35	Nicky-Leo Obreykov	BG	44.83	35	NA	NA	NA	EC	Male
2017	36	Mario-Rafaël Ionian	AT	42.62	36	NA	NA	NA	EC	Male
2017	1	Evgenia Medvedeva	RU	76.24	1	144.41	1	220.65	GPCAN	Female
2017	2	Kaetlyn Osmond	CA	74.33	2	132.12	2	206.45	GPCAN	Female
2017	3	Satoko Miyahara	JP	65.24	5	126.84	3	192.08	GPCAN	Female
2017	4	Elizaveta Tuktamysheva	RU	66.79	3	121.20	5	187.99	GPCAN	Female
2017	5	Alaine Chartrand	CA	62.15	6	123.41	4	185.56	GPCAN	Female
2017	6	Rika Hongo	JP	65.75	4	105.44	8	171.19	GPCAN	Female
2017	7	Dabin Choi	KR	53.29	8	112.49	6	165.78	GPCAN	Female
2017	8	Nahyun Kim	KR	60.46	7	104.02	9	164.48	GPCAN	Female
2017	9	Mirai Nagasu	US	53.19	9	98.23	11	151.42	GPCAN	Female
2017	10	Joshi Helgesson	SE	49.72	10	100.05	10	149.77	GPCAN	Female
2017	11	Yuka Nagai	JP	40.39	11	107.17	7	147.56	GPCAN	Female
2017	1	Patrick Chan	CA	90.56	1	176.39	2	266.95	GPCAN	Male
2017	2	Yuzuru Hanyu	JP	79.65	4	183.41	1	263.06	GPCAN	Male
2017	3	Kevin Reynolds	CA	80.57	3	164.49	3	245.06	GPCAN	Male
2017	4	Michal Březina	CZ	70.36	9	157.06	4	227.42	GPCAN	Male
2017	5	Daniel Samohin	IL	74.62	5	151.91	7	226.53	GPCAN	Male
2017	6	Misha Ge	UZ	72.30	7	153.77	5	226.07	GPCAN	Male
2017	7	Alexander Petrov	RU	71.50	8	152.89	6	224.39	GPCAN	Male
2017	8	Takahito Mura	JP	81.24	2	140.89	9	222.13	GPCAN	Male
2017	9	Liam Firus	CA	70.09	10	140.80	10	210.89	GPCAN	Male
2017	10	Han Yan	CN	72.86	6	136.25	11	209.11	GPCAN	Male
2017	11	Grant Hochstein	US	60.20	12	144.49	8	204.69	GPCAN	Male
2017	12	Ross Miner	US	63.92	11	132.61	12	196.53	GPCAN	Male
2017	1	Elena Radionova	RU	70.75	2	135.15	1	205.90	GPCHN	Female
2017	2	Kaetlyn Osmond	CA	72.20	1	123.80	3	196.00	GPCHN	Female
2017	3	Elizaveta Tuktamysheva	RU	64.88	4	127.69	2	192.57	GPCHN	Female
2017	4	Mai Mihara	JP	68.48	3	122.44	4	190.92	GPCHN	Female
2017	5	Rika Hongo	JP	63.63	6	118.12	6	181.75	GPCHN	Female
2017	6	Ashley Wagner	US	64.36	5	117.02	7	181.38	GPCHN	Female
2017	7	Karen Chen	US	58.28	9	121.11	5	179.39	GPCHN	Female
2017	8	Zijun Li	CN	61.32	7	111.08	8	172.40	GPCHN	Female
2017	9	Courtney Hicks	US	59.86	8	103.78	9	163.64	GPCHN	Female
2017	10	Xiangning Li	CN	54.55	11	102.72	10	157.27	GPCHN	Female
2017	11	Ziquan Zhao	CN	58.20	10	90.92	12	149.12	GPCHN	Female
2017	12	Joshi Helgesson	SE	49.25	12	95.39	11	144.64	GPCHN	Female
2017	1	Patrick Chan	CA	83.41	3	196.31	1	279.72	GPCHN	Male
2017	2	Boyang Jin	CN	96.17	1	182.37	2	278.54	GPCHN	Male
2017	3	Sergei Voronov	RU	82.93	4	160.83	4	243.76	GPCHN	Male
2017	4	Max Aaron	US	81.67	5	161.07	3	242.74	GPCHN	Male
2017	5	Han Yan	CN	75.04	8	155.15	5	230.19	GPCHN	Male
2017	6	Alexander Petrov	RU	74.21	9	154.23	6	228.44	GPCHN	Male
2017	7	Maxim Kovtun	RU	70.10	10	151.33	7	221.43	GPCHN	Male
2017	8	Daniel Samohin	IL	83.47	2	130.04	10	213.51	GPCHN	Male
2017	9	Ross Miner	US	76.73	6	136.61	8	213.34	GPCHN	Male
2017	10	Michal Březina	CZ	75.86	7	135.91	9	211.77	GPCHN	Male
2017	1	Evgenia Medvedeva	RU	79.21	1	148.45	1	227.66	GPF	Female
2017	2	Satoko Miyahara	JP	74.64	3	143.69	2	218.33	GPF	Female
2017	3	Anna Pogorilaya	RU	73.29	4	143.18	3	216.47	GPF	Female
2017	4	Kaetlyn Osmond	CA	75.54	2	136.91	4	212.45	GPF	Female
2017	5	Maria Sotskova	RU	65.74	6	133.05	5	198.79	GPF	Female
2017	6	Elena Radionova	RU	68.98	5	119.83	6	188.81	GPF	Female
2017	1	Yuzuru Hanyu	JP	106.53	1	187.37	3	293.90	GPF	Male
2017	2	Nathan Chen	US	85.30	5	197.55	1	282.85	GPF	Male
2017	3	Shoma Uno	JP	86.82	4	195.69	2	282.51	GPF	Male
2017	4	Javier Fernández	ES	91.76	3	177.01	4	268.77	GPF	Male
2017	5	Patrick Chan	CA	99.76	2	166.99	5	266.75	GPF	Male
2017	6	Adam Rippon	US	83.93	6	149.17	6	233.10	GPF	Male
2017	1	Evgenia Medvedeva	RU	78.52	1	143.02	1	221.54	GPFRA	Female
2017	2	Maria Sotskova	RU	68.71	3	131.64	2	200.35	GPFRA	Female
2017	3	Wakaba Higuchi	JP	65.02	5	129.46	3	194.48	GPFRA	Female
2017	4	Gabrielle Daleman	CA	72.70	2	119.40	6	192.10	GPFRA	Female
2017	5	Soyoun Park	KR	64.89	6	120.30	4	185.19	GPFRA	Female
2017	6	Laurine Lecavelier	FR	66.61	4	118.04	7	184.65	GPFRA	Female
2017	7	Maé-Bérénice Méité	FR	52.78	11	119.87	5	172.65	GPFRA	Female
2017	8	Gracie Gold	US	54.87	10	111.02	8	165.89	GPFRA	Female
2017	9	Mao Asada	JP	61.29	8	100.10	10	161.39	GPFRA	Female
2017	10	Yuka Nagai	JP	52.41	12	107.08	9	159.49	GPFRA	Female
2017	11	Anastasiya Galustyan	AM	56.92	9	98.57	11	155.49	GPFRA	Female
2017	12	Alena Leonova	RU	63.87	7	77.49	12	141.36	GPFRA	Female
2017	1	Javier Fernández	ES	96.57	1	188.81	1	285.38	GPFRA	Male
2017	2	Denis Ten	KZ	89.21	3	180.05	3	269.26	GPFRA	Male
2017	3	Adam Rippon	US	85.25	4	182.28	2	267.53	GPFRA	Male
2017	4	Nathan Chen	US	92.85	2	171.95	4	264.80	GPFRA	Male
2017	5	Takahito Mura	JP	78.38	6	170.04	5	248.42	GPFRA	Male
2017	6	Jorik Hendrickx	BE	80.34	5	150.13	8	230.47	GPFRA	Male
2017	7	Misha Ge	UZ	72.49	8	156.57	6	229.06	GPFRA	Male
2017	8	Chafik Besseghier	FR	77.00	7	148.02	9	225.02	GPFRA	Male
2017	9	Artur Dmitriev	RU	64.48	11	154.22	7	218.70	GPFRA	Male
2017	10	Brendan Kerry	AU	70.67	9	128.73	10	199.40	GPFRA	Male
2017	11	Ivan Righini	IT	68.42	10	117.39	11	185.81	GPFRA	Male
2017	1	Anna Pogorilaya	RU	71.56	1	139.30	1	210.86	GPJPN	Female
2017	2	Satoko Miyahara	JP	64.20	3	133.80	2	198.00	GPJPN	Female
2017	3	Maria Sotskova	RU	69.96	2	125.92	3	195.88	GPJPN	Female
2017	4	Wakaba Higuchi	JP	62.58	5	122.81	4	185.39	GPJPN	Female
2017	5	Mirai Nagasu	US	63.49	4	116.84	8	180.33	GPJPN	Female
2017	6	Karen Chen	US	58.76	7	119.69	5	178.45	GPJPN	Female
2017	7	Yura Matsuda	JP	60.98	6	117.28	7	178.26	GPJPN	Female
2017	8	Elizabet Tursynbayeva	KZ	55.66	9	119.45	6	175.11	GPJPN	Female
2017	9	Dabin Choi	KR	51.06	11	114.57	9	165.63	GPJPN	Female
2017	10	Alaine Chartrand	CA	58.72	8	101.50	11	160.22	GPJPN	Female
2017	11	Nicole Rajičová	SK	53.43	10	106.27	10	159.70	GPJPN	Female
2017	1	Yuzuru Hanyu	JP	103.89	1	197.58	1	301.47	GPJPN	Male
2017	2	Nathan Chen	US	87.94	2	180.97	2	268.91	GPJPN	Male
2017	3	Keiji Tanaka	JP	80.49	3	167.95	3	248.44	GPJPN	Male
2017	4	Alexei Bychenko	IL	75.13	7	154.74	4	229.87	GPJPN	Male
2017	5	Mikhail Kolyada	RU	78.18	4	147.51	6	225.69	GPJPN	Male
2017	6	Deniss Vasiljevs	LV	70.50	10	153.23	5	223.73	GPJPN	Male
2017	7	Jason Brown	US	74.33	8	144.14	7	218.47	GPJPN	Male
2017	8	Nam Nguyen	CA	75.33	6	137.10	8	212.43	GPJPN	Male
2017	9	Ryuju Hino	JP	72.50	9	134.65	9	207.15	GPJPN	Male
2017	10	Elladj Baldé	CA	76.29	5	119.03	11	195.32	GPJPN	Male
2017	11	Grant Hochstein	US	68.31	11	123.09	10	191.40	GPJPN	Male
2017	1	Anna Pogorilaya	RU	73.93	1	141.28	1	215.21	GPRUS	Female
2017	2	Elena Radionova	RU	71.93	2	123.67	2	195.60	GPRUS	Female
2017	3	Courtney Hicks	US	63.68	6	119.30	3	182.98	GPRUS	Female
2017	4	Zijun Li	CN	63.89	5	117.94	4	181.83	GPRUS	Female
2017	5	Elizabet Tursynbayeva	KZ	64.31	4	117.01	5	181.32	GPRUS	Female
2017	6	Yura Matsuda	JP	61.57	7	116.08	6	177.65	GPRUS	Female
2017	7	Nicole Rajičová	SK	57.91	8	109.65	7	167.56	GPRUS	Female
2017	8	Roberta Rodeghiero	IT	52.57	12	107.23	8	159.80	GPRUS	Female
2017	9	Anastasiya Galustyan	AM	55.93	9	103.33	9	159.26	GPRUS	Female
2017	10	Angelina Kuchvalska	LV	54.29	11	96.80	10	151.09	GPRUS	Female
2017	11	Kanako Murakami	JP	55.25	10	95.78	11	151.03	GPRUS	Female
2017	12	Julia Lipnitskaia	RU	69.25	3	78.88	12	148.13	GPRUS	Female
2017	1	Javier Fernández	ES	91.55	2	201.43	1	292.98	GPRUS	Male
2017	2	Shoma Uno	JP	98.59	1	186.48	2	285.07	GPRUS	Male
2017	3	Alexei Bychenko	IL	86.81	4	168.71	3	255.52	GPRUS	Male
2017	4	Mikhail Kolyada	RU	90.28	3	155.02	6	245.30	GPRUS	Male
2017	5	Max Aaron	US	73.64	8	161.94	4	235.58	GPRUS	Male
2017	6	Elladj Baldé	CA	76.36	6	149.09	8	225.45	GPRUS	Male
2017	7	Keiji Tanaka	JP	69.13	10	155.78	5	224.91	GPRUS	Male
2017	8	Chafik Besseghier	FR	80.68	5	143.30	10	223.98	GPRUS	Male
2017	9	Gordey Gorshkov	RU	73.37	9	150.14	7	223.51	GPRUS	Male
2017	10	Artur Dmitriev	RU	76.06	7	145.46	9	221.52	GPRUS	Male
2017	11	Deniss Vasiljevs	LV	62.40	12	141.37	11	203.77	GPRUS	Male
2017	12	Alexander Majorov	SE	67.80	11	124.34	12	192.14	GPRUS	Male
2017	1	Ashley Wagner	US	69.50	1	126.94	2	196.44	GPUSA	Female
2017	2	Mariah Bell	US	60.92	6	130.67	1	191.59	GPUSA	Female
2017	3	Mai Mihara	JP	65.75	2	123.53	3	189.28	GPUSA	Female
2017	4	Gabrielle Daleman	CA	64.49	4	122.14	4	186.63	GPUSA	Female
2017	5	Gracie Gold	US	64.87	3	119.35	5	184.22	GPUSA	Female
2017	6	Mao Asada	JP	64.47	5	112.31	6	176.78	GPUSA	Female
2017	7	Serafima Sakhanovich	RU	56.52	8	107.32	7	163.84	GPUSA	Female
2017	8	Soyoun Park	KR	58.16	7	103.20	8	161.36	GPUSA	Female
2017	9	Roberta Rodeghiero	IT	52.62	9	96.51	10	149.13	GPUSA	Female
2017	10	Kanako Murakami	JP	47.87	10	97.16	9	145.03	GPUSA	Female
2017	11	Angelina Kuchvalska	LV	47.80	11	87.17	11	134.97	GPUSA	Female
2017	1	Shoma Uno	JP	89.15	1	190.19	1	279.34	GPUSA	Male
2017	2	Jason Brown	US	85.75	3	182.63	2	268.38	GPUSA	Male
2017	3	Adam Rippon	US	87.32	2	174.11	3	261.43	GPUSA	Male
2017	4	Sergei Voronov	RU	78.68	5	166.60	5	245.28	GPUSA	Male
2017	5	Boyang Jin	CN	72.93	8	172.15	4	245.08	GPUSA	Male
2017	6	Nam Nguyen	CA	79.62	4	159.64	7	239.26	GPUSA	Male
2017	7	Maxim Kovtun	RU	67.43	10	163.32	6	230.75	GPUSA	Male
2017	8	Timothy Dolensky	US	77.59	6	148.94	8	226.53	GPUSA	Male
2017	9	Jorik Hendrickx	BE	76.62	7	148.29	9	224.91	GPUSA	Male
2017	10	Brendan Kerry	AU	71.62	9	140.14	10	211.76	GPUSA	Male

I also merged all the score data from the 5 specified World Championships, adding a year variable for the year the Championships took place.

#merge worlds data
worldmerged <- dplyr::bind_rows(list(world23, world22, world19, world18, world17), .id = 'year') 

#clean names
worldmerged$Skater <- gsub('[0-9]', '', worldmerged$Skater)
worldmerged$Skater <- str_squish(worldmerged$Skater)
colnames(worldmerged) <- c('year', 'worldrank', 'skater', 'nation', 'sp_score', 'sp_rank', 'fs_score', 'fs_rank', 'worldscore')

#add year variable
worldmerged <- worldmerged %>% 
  mutate(year = ifelse(year == 1, 2023,
                ifelse(year == 2, 2022,
                ifelse(year == 3, 2019, 
                ifelse(year == 4, 2018, 2017)))))

worldmerged %>%  kable() %>% 
  kable_styling(full_width = F) %>% 
  scroll_box(width = "100%", height = "200px")

year	worldrank	skater	nation	sp_score	sp_rank	fs_score	fs_rank	worldscore
2023	1	Shoma Uno	🇯🇵	104.63	1	196.51	1	301.14
2023	2	Junhwan Cha	🇰🇷	99.64	3	196.39	2	296.03
2023	3	Ilia Malinin	🇺🇸	100.38	2	188.06	3	288.44
2023	4	Kévin Aymoz	🇫🇷	95.56	5	187.41	4	282.97
2023	5	Jason Brown	🇺🇸	94.17	6	185.87	5	280.04
2023	6	Kazuki Tomono	🇯🇵	92.68	7	180.73	6	273.41
2023	7	Keegan Messing	🇨🇦	98.75	4	166.41	11	265.16
2023	8	Lukas Britschgi	🇨🇭	86.18	9	171.16	9	257.34
2023	9	Matteo Rizzo	🇮🇹	79.28	13	176.76	7	256.04
2023	10	Adam Siao Him Fa	🇫🇷	79.78	12	173.33	8	253.11
2023	11	Vladimir Litvintsev	🇦🇿	82.71	10	169.05	10	251.76
2023	12	Daniel Grassl	🇮🇹	86.50	8	157.93	14	244.43
2023	13	Deniss Vasiljevs	🇱🇻	82.37	11	160.78	13	243.15
2023	14	Mikhail Shaidorov	🇰🇿	75.41	18	161.52	12	236.93
2023	15	Sota Yamamoto	🇯🇵	75.48	17	156.91	15	232.39
2023	16	Mark Gorodnitsky	🇮🇱	77.89	14	154.24	16	232.13
2023	17	Mihhail Selevko	🇪🇪	76.81	15	154.13	17	230.94
2023	18	Andreas Nordebäck	🇸🇪	73.45	20	150.07	18	223.52
2023	19	Nikita Starostin	🇩🇪	75.53	16	142.34	19	217.87
2023	20	Morisi Kvitelashvili	🇬🇪	73.05	21	139.27	20	212.32
2023	21	Andrew Torgashev	🇺🇸	71.41	22	139.18	21	210.59
2023	22	Boyang Jin	🇨🇳	75.04	19	129.18	23	204.22
2023	23	Adam Hagara	🇸🇰	70.29	24	132.97	22	203.26
2023	24	Maurizio Zandrón	🇦🇹	70.36	23	123.95	24	194.31
2023	25	Kyrylo Marsak	🇺🇦	68.60	25	NA	NA	NA
2023	26	Conrad Orzel	🇨🇦	67.65	26	NA	NA	NA
2023	27	Tomás Guarino Sabaté	🇪🇸	67.60	27	NA	NA	NA
2023	28	Burak Demirboga	🇹🇷	65.73	28	NA	NA	NA
2023	29	Nika Egadze	🇬🇪	65.17	29	NA	NA	NA
2023	30	Alexander Zlatkov	🇧🇬	62.31	30	NA	NA	NA
2023	31	Jari Kessler	🇭🇷	61.94	31	NA	NA	NA
2023	32	Graham Newberry	🇬🇧	61.70	32	NA	NA	NA
2023	33	Vladimir Samoilov	🇵🇱	61.48	33	NA	NA	NA
2023	34	Georgiy Reshtenko	🇨🇿	59.93	34	NA	NA	NA
2023	1	Kaori Sakamoto	🇯🇵	79.24	1	145.37	2	224.61
2023	2	Haein Lee	🇰🇷	73.62	2	147.32	1	220.94
2023	3	Loena Hendrickx	🇧🇪	71.94	5	138.48	4	210.42
2023	4	Isabeau Levito	🇺🇸	73.03	4	134.62	5	207.65
2023	5	Mai Mihara	🇯🇵	73.46	3	132.24	6	205.70
2023	6	Chaeyeon Kim	🇰🇷	64.06	12	139.45	3	203.51
2023	7	Nicole Schott	🇩🇪	67.29	7	130.47	9	197.76
2023	8	Kimmy Repond	🇨🇭	62.75	13	131.34	8	194.09
2023	9	Niina Petrõkina	🇪🇪	68.00	6	125.49	12	193.49
2023	10	Rinka Watanabe	🇯🇵	60.90	15	131.91	7	192.81
2023	11	Nina Pinzarrone	🇧🇪	62.04	14	129.74	10	191.78
2023	12	Amber Glenn	🇺🇸	65.52	10	122.81	14	188.33
2023	13	Madeline Schizas	🇨🇦	60.02	16	127.47	11	187.49
2023	14	Anastasiia Gubanova	🇬🇪	65.40	11	119.52	15	184.92
2023	15	Bradie Tennell	🇺🇸	66.45	8	117.69	16	184.14
2023	16	Ekaterina Kurakova	🇵🇱	65.69	9	115.74	17	181.43
2023	17	Lara Naki Gutmann	🇮🇹	55.22	23	123.21	13	178.43
2023	18	Yelim Kim	🇰🇷	60.02	17	114.28	19	174.30
2023	19	Olga Mikutina	🇦🇹	57.05	20	115.26	18	172.31
2023	20	Julia Sauter	🇷🇴	56.02	22	109.60	20	165.62
2023	21	Janna Jyrkinen	🇫🇮	56.06	21	104.85	21	160.91
2023	22	Lindsay Van Zundert	🇳🇱	57.56	19	101.99	22	159.55
2023	23	Sofja Stepchenko	🇱🇻	58.87	18	99.51	24	158.38
2023	24	Alexandra Feigin	🇧🇬	54.65	24	101.09	23	155.74
2023	25	Lorine Schild	🇫🇷	54.35	25	NA	NA	NA
2023	26	Jade Hovine	🇧🇪	54.10	26	NA	NA	NA
2023	27	Kristen Spours	🇬🇧	53.38	27	NA	NA	NA
2023	28	Ema Doboszova	🇸🇰	53.01	28	NA	NA	NA
2023	29	Kristina Isaev	🇩🇪	52.93	29	NA	NA	NA
2023	30	Anastasia Gracheva	🇲🇩	50.55	30	NA	NA	NA
2023	31	Marilena Kitromilis	🇨🇾	48.92	31	NA	NA	NA
2023	32	Eliska Brezinova	🇨🇿	47.29	32	NA	NA	NA
2023	33	Daša Grm	🇸🇮	47.04	33	NA	NA	NA
2023	34	Júlia Láng	🇭🇺	44.26	34	NA	NA	NA
2023	35	Mia Caroline Risa Gomez	🇳🇴	43.54	35	NA	NA	NA
2022	1	Shoma Uno	🇯🇵	109.63	1	202.85	1	312.48
2022	2	Yuma Kagiyama	🇯🇵	105.69	2	191.91	2	297.60
2022	3	Vincent Zhou	🇺🇸	95.84	6	181.54	4	277.38
2022	4	Morisi Kvitelashvili	🇬🇪	92.61	7	179.42	5	272.03
2022	5	Camden Pulkinen	🇺🇸	89.50	12	182.19	3	271.69
2022	6	Kazuki Tomono	🇯🇵	101.12	3	168.25	8	269.37
2022	7	Daniel Grassl	🇮🇹	97.62	5	169.04	7	266.66
2022	8	Adam Siao Him Fa	🇫🇷	90.97	10	175.15	6	266.12
2022	9	Ilia Malinin	🇺🇸	100.16	4	163.63	11	263.79
2022	10	Matteo Rizzo	🇮🇹	91.67	8	164.08	10	255.75
2022	11	Kévin Aymoz	🇫🇷	85.26	15	160.20	12	245.46
2022	12	Roman Sadovsky	🇨🇦	80.54	18	164.82	9	245.36
2022	13	Deniss Vasiljevs	🇱🇻	90.95	11	152.05	14	243.00
2022	14	Keegan Messing	🇨🇦	91.18	9	143.85	17	235.03
2022	15	Mihhail Selevko	🇪🇪	78.85	20	155.87	13	234.72
2022	16	Vladimir Litvintsev	🇦🇿	85.83	14	147.79	15	233.62
2022	17	Maurizio Zandrón	🇦🇹	83.10	16	145.17	16	228.27
2022	18	Sihyeong Lee	🇰🇷	86.35	13	138.71	18	225.06
2022	19	Nikolaj Majorov	🇸🇪	79.36	19	137.09	20	216.45
2022	20	Graham Newberry	🇬🇧	74.92	21	135.48	21	210.40
2022	21	Tomás Guarino Sabaté	🇪🇸	71.42	24	137.53	19	208.95
2022	22	Nikita Starostin	🇩🇪	73.79	23	131.93	22	205.72
2022	23	Ivan Shmuratko	🇺🇦	73.99	22	122.66	23	196.65
2022	NA	Junhwan Cha	🇰🇷	82.43	17	NA	NA	NA
2022	25	Mark Gorodnitsky	🇮🇱	69.70	25	NA	NA	NA
2022	26	Adam Hagara	🇸🇰	60.92	26	NA	NA	NA
2022	27	Vladimir Samoilov	🇵🇱	60.71	27	NA	NA	NA
2022	28	Burak Demirboga	🇹🇷	52.86	28	NA	NA	NA
2022	29	Aleksandr Vlasenko	🇭🇺	51.10	29	NA	NA	NA
2022	1	Kaori Sakamoto	🇯🇵	80.32	1	155.77	1	236.09
2022	2	Loena Hendrickx	🇧🇪	75.00	2	142.70	2	217.70
2022	3	Alysa Liu	🇺🇸	71.91	5	139.28	3	211.19
2022	4	Mariah Bell	🇺🇸	72.55	3	136.11	4	208.66
2022	5	Young You	🇰🇷	72.08	4	132.83	6	204.91
2022	6	Anastasiia Gubanova	🇬🇪	62.59	14	134.02	5	196.61
2022	7	Haein Lee	🇰🇷	64.16	11	132.39	7	196.55
2022	8	Karen Chen	🇺🇸	66.16	8	126.35	8	192.51
2022	9	Ekaterina Ryabova	🇦🇿	65.52	9	122.98	11	188.50
2022	10	Nicole Schott	🇩🇪	67.77	6	120.65	14	188.42
2022	11	Wakaba Higuchi	🇯🇵	67.03	7	121.12	12	188.15
2022	12	Madeline Schizas	🇨🇦	64.20	10	123.94	10	188.14
2022	13	Ekaterina Kurakova	🇵🇱	61.92	16	124.51	9	186.43
2022	14	Olga Mikutina	🇦🇹	62.14	15	120.84	13	182.98
2022	15	Mana Kawabe	🇯🇵	63.68	12	118.76	15	182.44
2022	16	Niina Petrõkina	🇪🇪	60.24	17	116.36	16	176.60
2022	17	Lindsay Van Zundert	🇳🇱	58.49	18	112.90	17	171.39
2022	18	Julia Sauter	🇷🇴	58.07	19	112.24	18	170.31
2022	19	Alexia Paganini	🇨🇭	63.09	13	106.93	19	170.02
2022	20	Lara Naki Gutmann	🇮🇹	57.92	20	106.47	20	164.39
2022	21	Josefin Taljegård	🇸🇪	57.52	21	105.72	21	163.24
2022	22	Kailani Craine	🇦🇺	56.64	22	105.11	22	161.75
2022	23	Natasha Mckay	🇬🇧	55.71	24	103.56	23	159.27
2022	24	Daša Grm	🇸🇮	55.82	23	91.30	24	147.12
2022	25	Jenni Saarinen	🇫🇮	55.30	25	NA	NA	NA
2022	26	Tzu-Han Ting	🇹🇼	55.24	26	NA	NA	NA
2022	27	Eliska Brezinova	🇨🇿	55.07	27	NA	NA	NA
2022	28	Alexandra Feigin	🇧🇬	55.01	28	NA	NA	NA
2022	29	Léa Serna	🇫🇷	54.30	29	NA	NA	NA
2022	30	Marilena Kitromilis	🇨🇾	53.32	30	NA	NA	NA
2022	31	Júlia Láng	🇭🇺	47.93	31	NA	NA	NA
2022	32	Stefanie Pesendorfer	🇦🇹	47.23	32	NA	NA	NA
2022	33	Anete Lace	🇱🇻	44.60	33	NA	NA	NA
2019	1	Nathan Chen	🇺🇸	107.40	1	216.02	1	323.42
2019	2	Yuzuru Hanyu	🇯🇵	94.87	3	206.10	2	300.97
2019	3	Vincent Zhou	🇺🇸	94.17	4	186.99	3	281.16
2019	4	Shoma Uno	🇯🇵	91.40	6	178.92	4	270.32
2019	5	Boyang Jin	🇨🇳	84.26	9	178.45	5	262.71
2019	6	Mikhail Kolyada	🇷🇺	84.23	10	178.21	6	262.44
2019	7	Matteo Rizzo	🇮🇹	93.37	5	164.29	10	257.66
2019	8	Michal Březina	🇨🇿	86.96	8	167.32	8	254.28
2019	9	Jason Brown	🇺🇸	96.81	2	157.34	14	254.15
2019	10	Andrei Lazukin	🇷🇺	84.05	11	164.69	9	248.74
2019	11	Kévin Aymoz	🇫🇷	88.24	7	159.23	12	247.47
2019	12	Alexander Samarin	🇷🇺	78.38	20	167.95	7	246.33
2019	13	Morisi Kvitelashvili	🇬🇪	82.67	12	158.07	13	240.74
2019	14	Keiji Tanaka	🇯🇵	78.76	19	159.64	11	238.40
2019	15	Keegan Messing	🇨🇦	82.38	14	155.26	15	237.64
2019	16	Nam Nguyen	🇨🇦	82.51	13	154.76	16	237.27
2019	17	Vladimir Litvintsev	🇦🇿	81.46	16	149.38	19	230.84
2019	18	Alexander Majorov	🇸🇪	79.17	17	150.55	17	229.72
2019	19	Junhwan Cha	🇰🇷	79.17	18	150.09	18	229.26
2019	20	Brendan Kerry	🇦🇺	78.26	21	143.76	21	222.02
2019	21	Deniss Vasiljevs	🇱🇻	74.74	23	143.78	20	218.52
2019	22	Alexei Bychenko	🇮🇱	77.67	22	138.93	22	216.60
2019	23	Julian Zhi-Jie Yee	🇲🇾	73.63	24	132.34	23	205.97
2019	24	Daniel Samohin	🇮🇱	82.00	15	123.28	24	205.28
2019	25	Peter James Hallam	🇬🇧	66.06	25	NA	NA	NA
2019	26	Luc Maierhofer	🇦🇹	65.78	26	NA	NA	NA
2019	27	Aleksandr Selevko	🇪🇪	63.25	27	NA	NA	NA
2019	28	Paul Fentz	🇩🇪	63.24	28	NA	NA	NA
2019	29	Ivan Shmuratko	🇺🇦	62.99	29	NA	NA	NA
2019	30	Burak Demirboga	🇹🇷	60.79	30	NA	NA	NA
2019	31	Slavik Hayrapetyan	🇦🇲	60.66	31	NA	NA	NA
2019	32	Valtter Virtanen	🇫🇮	55.73	32	NA	NA	NA
2019	33	Donovan Carrillo	🇲🇽	54.99	33	NA	NA	NA
2019	34	Lukas Britschgi	🇨🇭	54.58	34	NA	NA	NA
2019	35	Ihor Reznichenko	🇵🇱	50.15	35	NA	NA	NA
2019	1	Alina Zagitova	🇷🇺	82.08	1	155.42	1	237.50
2019	2	Elizabet Tursynbayeva	🇰🇿	75.96	3	148.80	4	224.76
2019	3	Evgenia Medvedeva	🇷🇺	74.23	4	149.57	3	223.80
2019	4	Rika Kihira	🇯🇵	70.90	7	152.59	2	223.49
2019	5	Kaori Sakamoto	🇯🇵	76.86	2	145.97	5	222.83
2019	6	Satoko Miyahara	🇯🇵	70.60	8	145.35	6	215.95
2019	7	Bradie Tennell	🇺🇸	69.50	10	143.97	7	213.47
2019	8	Sofia Samodurova	🇷🇺	70.42	9	138.16	8	208.58
2019	9	Mariah Bell	🇺🇸	71.26	6	136.81	9	208.07
2019	10	Eunsoo Lim	🇰🇷	72.91	5	132.66	10	205.57
2019	11	Gabrielle Daleman	🇨🇦	69.19	11	123.48	12	192.67
2019	12	Loena Hendrickx	🇧🇪	62.60	13	123.69	11	186.29
2019	13	Ekaterina Ryabova	🇦🇿	57.18	17	122.70	13	179.88
2019	14	Yi Christy Leung	🇭🇰	58.60	14	118.62	14	177.22
2019	15	Laurine Lecavelier	🇫🇷	56.81	19	113.78	15	170.59
2019	16	Nicole Schott	🇩🇪	63.18	12	107.38	17	170.56
2019	17	Alexandra Feigin	🇧🇬	56.69	20	108.62	16	165.31
2019	18	Daša Grm	🇸🇮	57.58	16	103.58	18	161.16
2019	19	Hongyi Chen	🇨🇳	58.53	15	99.06	19	157.59
2019	20	Eliska Brezinova	🇨🇿	57.13	18	96.32	20	153.45
2019	21	Natasha Mckay	🇬🇧	56.40	21	95.16	21	151.56
2019	22	Eva Lotta Kiibus	🇪🇪	55.38	23	94.61	22	149.99
2019	23	Alaine Chartrand	🇨🇦	55.89	22	93.08	23	148.97
2019	24	Isadora Williams	🇧🇷	55.20	24	88.02	24	143.22
2019	25	Ivett Tóth	🇭🇺	54.87	25	NA	NA	NA
2019	26	Pernille Sørensen	🇩🇰	54.36	26	NA	NA	NA
2019	27	Marina Piredda	🇮🇹	53.27	27	NA	NA	NA
2019	28	Emmi Peltonen	🇫🇮	53.22	28	NA	NA	NA
2019	29	Julia Sauter	🇷🇴	53.11	29	NA	NA	NA
2019	30	Anita Östlund	🇸🇪	53.07	30	NA	NA	NA
2019	31	Roberta Rodeghiero	🇮🇹	51.50	31	NA	NA	NA
2019	32	Nicole Rajičová	🇸🇰	51.22	32	NA	NA	NA
2019	33	Alexia Paganini	🇨🇭	50.51	33	NA	NA	NA
2019	34	Valentina Matos	🇪🇸	50.25	34	NA	NA	NA
2019	35	Aurora Cotop	🇨🇦	48.83	35	NA	NA	NA
2019	36	Kailani Craine	🇦🇺	48.82	36	NA	NA	NA
2019	37	Sophia Schaller	🇦🇹	48.72	37	NA	NA	NA
2019	38	Elzbieta Kropa	🇱🇹	47.95	38	NA	NA	NA
2019	39	Anastasiya Galustyan	🇦🇲	47.75	39	NA	NA	NA
2019	40	Kyarha Van Tiel	🇳🇱	41.85	40	NA	NA	NA
2018	1	Nathan Chen	🇺🇸	101.94	1	219.46	1	321.40
2018	2	Shoma Uno	🇯🇵	94.26	5	179.51	2	273.77
2018	3	Mikhail Kolyada	🇷🇺	100.08	2	172.24	4	272.32
2018	4	Alexei Bychenko	🇮🇱	90.99	7	167.29	7	258.28
2018	5	Kazuki Tomono	🇯🇵	82.61	11	173.50	3	256.11
2018	6	Deniss Vasiljevs	🇱🇻	84.25	9	170.61	5	254.86
2018	7	Dmitri Aliev	🇷🇺	82.15	13	170.15	6	252.30
2018	8	Keegan Messing	🇨🇦	93.00	6	159.30	11	252.30
2018	9	Misha Ge	🇺🇿	86.01	8	163.56	9	249.57
2018	10	Michal Březina	🇨🇿	78.01	17	165.98	8	243.99
2018	11	Max Aaron	🇺🇸	79.78	15	161.71	10	241.49
2018	12	Alexander Majorov	🇸🇪	82.71	10	155.08	13	237.79
2018	13	Keiji Tanaka	🇯🇵	80.17	14	156.49	12	236.66
2018	14	Vincent Zhou	🇺🇸	96.78	3	138.46	19	235.24
2018	15	Paul Fentz	🇩🇪	82.49	12	148.43	16	230.92
2018	16	Romain Ponsart	🇫🇷	79.55	16	149.65	14	229.20
2018	17	Matteo Rizzo	🇮🇹	77.43	18	148.01	17	225.44
2018	18	Brendan Kerry	🇦🇺	74.99	19	148.86	15	223.85
2018	19	Boyang Jin	🇨🇳	95.85	4	127.56	23	223.41
2018	20	Daniel Samohin	🇮🇱	72.78	20	141.23	18	214.01
2018	21	Julian Zhi-Jie Yee	🇲🇾	72.43	21	136.60	20	209.03
2018	22	Donovan Carrillo	🇲🇽	68.13	24	132.63	21	200.76
2018	23	Slavik Hayrapetyan	🇦🇲	68.18	23	131.54	22	199.72
2018	24	Phillip Harris	🇬🇧	68.59	22	119.10	24	187.69
2018	25	Nam Nguyen	🇨🇦	67.79	25	NA	NA	NA
2018	26	Morisi Kvitelashvili	🇬🇪	67.01	26	NA	NA	NA
2018	27	Stéphane Walker	🇨🇭	65.79	27	NA	NA	NA
2018	28	Burak Demirboga	🇹🇷	65.43	28	NA	NA	NA
2018	29	Ivan Pavlov	🇺🇦	64.18	29	NA	NA	NA
2018	30	Chih-I Tsao	🇹🇼	64.06	30	NA	NA	NA
2018	31	Larry Loupolover	🇦🇿	61.82	31	NA	NA	NA
2018	32	Abzal Rakimgaliev	🇰🇿	61.19	32	NA	NA	NA
2018	33	Jinseo Kim	🇰🇷	60.72	33	NA	NA	NA
2018	34	Nicholas Vrdoljak	🇭🇷	59.74	34	NA	NA	NA
2018	35	Valtter Virtanen	🇫🇮	55.49	35	NA	NA	NA
2018	36	Ihor Reznichenko	🇵🇱	51.70	36	NA	NA	NA
2018	37	Javier Raya	🇪🇸	50.00	37	NA	NA	NA
2018	1	Kaetlyn Osmond	🇨🇦	72.73	4	150.50	1	223.23
2018	2	Wakaba Higuchi	🇯🇵	65.89	8	145.01	2	210.90
2018	3	Satoko Miyahara	🇯🇵	74.36	3	135.72	3	210.08
2018	4	Carolina Kostner	🇮🇹	80.27	1	128.61	5	208.88
2018	5	Alina Zagitova	🇷🇺	79.51	2	128.21	7	207.72
2018	6	Bradie Tennell	🇺🇸	68.76	7	131.13	4	199.89
2018	7	Gabrielle Daleman	🇨🇦	71.61	6	125.11	8	196.72
2018	8	Maria Sotskova	🇷🇺	71.80	5	124.81	9	196.61
2018	9	Loena Hendrickx	🇧🇪	64.07	10	128.24	6	192.31
2018	10	Mirai Nagasu	🇺🇸	65.21	9	122.31	11	187.52
2018	11	Elizabet Tursynbayeva	🇰🇿	62.38	11	124.47	10	186.85
2018	12	Mariah Bell	🇺🇸	59.15	17	115.25	12	174.40
2018	13	Nicole Schott	🇩🇪	61.84	12	112.29	14	174.13
2018	14	Laurine Lecavelier	🇫🇷	59.79	15	113.44	13	173.23
2018	15	Hanul Kim	🇰🇷	60.14	14	110.54	15	170.68
2018	16	Viveca Lindfors	🇫🇮	60.18	13	106.05	16	166.23
2018	17	Kailani Craine	🇦🇺	56.90	20	97.51	18	154.41
2018	18	Eliska Brezinova	🇨🇿	58.37	18	94.77	19	153.14
2018	19	Stanislava Konstantinova	🇷🇺	59.19	16	93.84	20	153.03
2018	20	Alexia Paganini	🇨🇭	57.86	19	91.80	22	149.66
2018	21	Elisabetta Leccardi	🇮🇹	51.13	23	98.04	17	149.17
2018	22	Daša Grm	🇸🇮	52.43	22	92.08	21	144.51
2018	23	Ivett Tóth	🇭🇺	50.63	24	86.24	23	136.87
2018	NA	Dabin Choi	🇰🇷	55.30	21	NA	NA	NA
2018	25	Larkyn Austman	🇨🇦	50.17	25	NA	NA	NA
2018	26	Xiangning Li	🇨🇳	50.06	26	NA	NA	NA
2018	27	Nicole Rajičová	🇸🇰	49.87	27	NA	NA	NA
2018	28	Amy Lin	🇹🇼	49.31	28	NA	NA	NA
2018	29	Anita Östlund	🇸🇪	48.99	29	NA	NA	NA
2018	30	Alisa Stomakhina	🇦🇹	48.71	30	NA	NA	NA
2018	31	Elzbieta Kropa	🇱🇹	46.53	31	NA	NA	NA
2018	32	Natasha Mckay	🇬🇧	45.89	32	NA	NA	NA
2018	33	Anne Line Gjersem	🇳🇴	45.25	33	NA	NA	NA
2018	34	Gerli Liinamäe	🇪🇪	45.14	34	NA	NA	NA
2018	35	Isadora Williams	🇧🇷	42.16	35	NA	NA	NA
2018	36	Antonina Dubinina	🇷🇸	41.40	36	NA	NA	NA
2018	37	Angelina Kuchvalska	🇱🇻	35.78	37	NA	NA	NA
2017	1	Yuzuru Hanyu	🇯🇵	98.39	5	223.20	1	321.59
2017	2	Shoma Uno	🇯🇵	104.86	2	214.45	2	319.31
2017	3	Boyang Jin	🇨🇳	98.64	4	204.94	3	303.58
2017	4	Javier Fernández	🇪🇸	109.05	1	192.14	6	301.19
2017	5	Patrick Chan	🇨🇦	102.13	3	193.03	5	295.16
2017	6	Nathan Chen	🇺🇸	97.33	6	193.39	4	290.72
2017	7	Jason Brown	🇺🇸	93.10	8	176.47	7	269.57
2017	8	Mikhail Kolyada	🇷🇺	93.28	7	164.19	9	257.47
2017	9	Kevin Reynolds	🇨🇦	84.44	12	169.40	8	253.84
2017	10	Alexei Bychenko	🇮🇱	85.28	11	160.68	12	245.96
2017	11	Maxim Kovtun	🇷🇺	89.38	10	156.46	14	245.84
2017	12	Misha Ge	🇺🇿	79.91	16	163.54	10	243.45
2017	13	Morisi Kvitelashvili	🇬🇪	76.34	19	162.90	11	239.24
2017	14	Deniss Vasiljevs	🇱🇻	81.73	14	157.27	13	239.00
2017	15	Brendan Kerry	🇦🇺	83.11	13	153.13	15	236.24
2017	16	Denis Ten	🇰🇿	90.18	9	144.13	20	234.31
2017	17	Chafik Besseghier	🇫🇷	78.82	17	151.31	16	230.13
2017	18	Michal Březina	🇨🇿	80.02	15	146.24	18	226.26
2017	19	Keiji Tanaka	🇯🇵	73.45	22	148.89	17	222.34
2017	20	Paul Fentz	🇩🇪	73.89	20	144.02	21	217.91
2017	21	Jorik Hendrickx	🇧🇪	73.68	21	140.34	22	214.02
2017	22	Julian Zhi-Jie Yee	🇲🇾	69.74	23	144.25	19	213.99
2017	23	Alexander Majorov	🇸🇪	77.23	18	127.81	23	205.04
2017	24	Michael Christian Martinez	🇵🇭	69.32	24	127.47	24	196.79
2017	25	Ivan Pavlov	🇺🇦	69.26	25	NA	NA	NA
2017	26	Jinseo Kim	🇰🇷	68.66	26	NA	NA	NA
2017	27	Javier Raya	🇪🇸	66.88	27	NA	NA	NA
2017	28	Stéphane Walker	🇨🇭	64.04	28	NA	NA	NA
2017	29	Ihor Reznichenko	🇵🇱	63.88	29	NA	NA	NA
2017	30	Matteo Rizzo	🇮🇹	63.14	30	NA	NA	NA
2017	31	Graham Newberry	🇬🇧	62.04	31	NA	NA	NA
2017	32	Chih-I Tsao	🇹🇼	61.52	32	NA	NA	NA
2017	33	Valtter Virtanen	🇫🇮	59.45	33	NA	NA	NA
2017	34	Nicholas Vrdoljak	🇭🇷	57.28	34	NA	NA	NA
2017	35	Slavik Hayrapetyan	🇦🇲	57.14	35	NA	NA	NA
2017	36	Larry Loupolover	🇦🇿	38.97	36	NA	NA	NA
2017	1	Evgenia Medvedeva	🇷🇺	79.01	1	154.40	1	233.41
2017	2	Kaetlyn Osmond	🇨🇦	75.98	2	142.15	2	218.13
2017	3	Gabrielle Daleman	🇨🇦	72.19	3	141.33	3	213.52
2017	4	Karen Chen	🇺🇸	69.98	5	129.31	6	199.29
2017	5	Mai Mihara	🇯🇵	59.59	15	138.29	4	197.88
2017	6	Carolina Kostner	🇮🇹	66.33	8	130.50	5	196.83
2017	7	Ashley Wagner	🇺🇸	69.04	7	124.50	10	193.54
2017	8	Maria Sotskova	🇷🇺	69.76	6	122.44	11	192.20
2017	9	Elizabet Tursynbayeva	🇰🇿	65.48	10	126.51	8	191.99
2017	10	Dabin Choi	🇰🇷	62.66	11	128.45	7	191.11
2017	11	Wakaba Higuchi	🇯🇵	65.87	9	122.18	12	188.05
2017	12	Mariah Bell	🇺🇸	61.02	13	126.21	9	187.23
2017	13	Anna Pogorilaya	🇷🇺	71.52	4	111.85	15	183.37
2017	14	Xiangning Li	🇨🇳	58.28	16	117.09	13	175.37
2017	15	Loena Hendrickx	🇧🇪	57.54	17	115.28	14	172.82
2017	16	Rika Hongo	🇯🇵	62.55	12	107.28	18	169.83
2017	17	Nicole Rajičová	🇸🇰	57.08	18	108.47	16	165.55
2017	18	Laurine Lecavelier	🇫🇷	55.49	22	107.50	17	162.99
2017	19	Nicole Schott	🇩🇪	54.83	24	106.58	19	161.41
2017	20	Ivett Tóth	🇭🇺	61.00	14	99.77	21	160.77
2017	21	Zijun Li	🇨🇳	56.30	20	103.50	20	159.80
2017	22	Angelina Kuchvalska	🇱🇻	55.92	21	99.10	22	155.02
2017	23	Anastasiya Galustyan	🇦🇲	55.20	23	98.27	23	153.47
2017	24	Kailani Craine	🇦🇺	56.97	19	95.97	24	152.94
2017	25	Shuran Yu	🇸🇬	52.87	25	NA	NA	NA
2017	26	Joshi Helgesson	🇸🇪	52.07	26	NA	NA	NA
2017	27	Helery Hälvin	🇪🇪	51.94	27	NA	NA	NA
2017	28	Amy Lin	🇹🇼	51.86	28	NA	NA	NA
2017	29	Emmi Peltonen	🇫🇮	50.74	29	NA	NA	NA
2017	30	Isadora Williams	🇧🇷	50.65	30	NA	NA	NA
2017	31	Kerstin Frank	🇦🇹	50.54	31	NA	NA	NA
2017	32	Natasha Mckay	🇬🇧	50.10	32	NA	NA	NA
2017	33	Yasmine Kimiko Yamada	🇨🇭	47.86	33	NA	NA	NA
2017	34	Anne Line Gjersem	🇳🇴	46.99	34	NA	NA	NA
2017	35	Anna Khnychenkova	🇺🇦	46.98	35	NA	NA	NA
2017	36	Daša Grm	🇸🇮	46.63	36	NA	NA	NA
2017	37	Michaela Hanzlikova	🇨🇿	32.21	37	NA	NA	NA

Missing Data

Let’s explore if there are any missing data in our datasets first. Here is a missing data plot for our regular competition score dataset:

reg_merged %>% 
  vis_miss()

And for the Worlds dataset:

worldmerged %>% 
  vis_miss()

There seems to be a lot of missing data within the Free Skate score and rank, and the total score and final rank. Some of this could be because at bigger events, like the European Championships, 4 Continents championships, and Worlds Championships, only the top 24 skaters can advanced to the free skate. This leaves them with no free skate score or rank, and also doesn’t count towards their total score since they technically did not finish the competition. Here is an example below from the 2023 European Championships for women, where those ranked below 24 have missing data.

year	final_rank	skater	nation	sp_score	sp_rank	fs_score	fs_rank	total_score	competition	sex
2023	25	Alexandra Michaela Filcová	SK	43.94	25	NA	NA	NA	EC	Female
2023	26	Léa Serna	FR	43.93	26	NA	NA	NA	EC	Female
2023	27	Antonina Dubinina	RS	42.51	27	NA	NA	NA	EC	Female
2023	28	Anastasia Gracheva	MD	39.08	28	NA	NA	NA	EC	Female
2023	29	Alexandra Mintsidou	GR	33.86	29	NA	NA	NA	EC	Female

A missing final rank could be a skater withdrawing from the competition. An example would be Isabeau Levito during 2023 4 Continents. She did her short program but withdrew after because of sickness and therefore does not get ranked.

reg_merged %>% 
  filter(skater == 'Isabeau Levito', competition == '4CC', year == 2023)%>% 
  kable() %>% 
  kable_styling(full_width = F)

year	final_rank	skater	nation	sp_score	sp_rank	fs_score	fs_rank	total_score	competition	sex
2023	NA	Isabeau Levito	US	71.5	2	NA	NA	NA	4CC	Female

For the dataset of Worlds score, we see the same issues for missing data; however, I filled in their total score with their short program score if they reached that point since we need it for total World score as the response variable. We can say the free skate score was 0 since they did not get to do it, so their total score is just their short program score and they are ranked based on that. We can see this below with how the Worlds score between 24th and 25th place has a huge drop.

#fix missing total score
worldmerged$worldscore <- ifelse(is.na(worldmerged$worldscore), worldmerged$sp_score, worldmerged$worldscore)

#example
worldmerged %>% 
  filter(year == 2018, worldrank <=25, worldrank>=24, skater != "Larkyn Austman") %>% 
  kable() %>% 
  kable_styling(full_width = F) %>% 
  scroll_box(width = "100%", height = "200px")

year	worldrank	skater	nation	sp_score	sp_rank	fs_score	fs_rank	worldscore
2018	24	Phillip Harris	🇬🇧	68.59	22	119.1	24	187.69
2018	25	Nam Nguyen	🇨🇦	67.79	25	NA	NA	67.79

Tidying Data

First, I decided that I would separate average short program score and average free skate score, rather than just using average total score, for my analysis. This is because sometime skaters can do better in one program compared to the other. Free skates allow for higher scores to be earned, so if a skater typically falters during a free skate, we would like to see that reflected. It also helps our missing data problem, where any missing free skate scores won’t have too much of an effect as long as the skater has another free skate score in the season or the skater doesn’t reach the free skate during Worlds.

So I added 2 columns into the regular competition score data set that holds the average scores for each skater for that season. Then I pivoted the dataframe so that each competition has its own column and is marked 1 if the skater attended the event that season and a 0 if not. Finally, I merged the regular score dataframe with the Worlds score dataframe, dropped unneeded columns, and finally got for each season, each skater has one row with their name, the ending year of the season as a factor, their sex, their short program average, their free program average, factor variables that tells us which competition they went to in the season, and for the response variable for our predictions: their score at Worlds that year.

#take averages
avg <- reg_merged %>% 
  group_by(skater, year) %>% 
  mutate(spavg = mean(sp_score,na.rm = TRUE)) %>% 
  mutate(fpavg = mean(fs_score, na.rm=TRUE)) %>% 
  mutate(tsavg = mean(total_score, na.rm=TRUE)) %>%
  select(skater, year, competition, spavg, fpavg, tsavg, sex)

#pivot competition variable
avg$truth <- 1
pivoted <- avg %>% 
  pivot_wider(names_from = competition, values_from = truth)
pivoted[is.na(pivoted)] <- 0

#drop unneeded columns in worldmerged
worldmerged <- worldmerged %>% 
  select(year, skater, worldscore, worldrank)

#for graphing later on
graphdata <- merge(worldmerged, pivoted, by = c('skater', 'year'))
graphdata$tsavg <- ifelse(graphdata$tsavg==0, graphdata$spavg, graphdata$tsavg)

#final dataframe for analysis
data <- graphdata %>% 
  select(-tsavg, - worldrank) %>% 
  mutate(year = as.factor(year))

#factor competition variables
for (i in c(7:19)) {
  data[, i] <- factor(data[, i])
}

data %>% kable() %>% 
  kable_styling(full_width = F) %>% 
  scroll_box(width = "100%", height = "200px")

skater	year	worldscore	spavg	fpavg	sex	GPUSA	GPJPN	GPGBR	GPFRA	GPFIN	GPF	GPCAN	EC	4CC	OLY	GPRUS	GPITA	GPCHN
Abzal Rakimgaliev	2018	61.19	60.77000	114.81000	Male	0	0	0	0	0	0	0	0	1	0	0	0	0
Adam Hagara	2022	60.92	65.23000	0.00000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Adam Hagara	2023	203.26	65.15000	124.57000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Adam Siao Him Fa	2022	266.12	79.60333	157.38333	Male	1	0	0	1	0	0	0	0	0	1	0	0	0
Adam Siao Him Fa	2023	253.11	90.65667	171.74333	Male	0	1	0	1	0	0	0	1	0	0	0	0	0
Alaine Chartrand	2019	148.97	51.11667	107.28333	Female	1	0	0	0	0	0	1	0	1	0	0	0	0
Aleksandr Selevko	2019	63.25	69.94000	125.19000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Alexander Majorov	2017	205.04	73.33500	131.72500	Male	0	0	0	0	0	0	0	1	0	0	1	0	0
Alexander Majorov	2018	237.79	67.77500	138.17500	Male	0	0	0	0	0	0	0	1	0	0	0	0	1
Alexander Majorov	2019	229.72	82.28333	134.80667	Male	0	0	0	0	0	0	1	1	0	0	1	0	0
Alexander Samarin	2019	246.33	90.29667	164.94000	Male	0	0	0	1	0	0	1	1	0	0	0	0	0
Alexandra Feigin	2019	165.31	58.80000	105.40000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Alexandra Feigin	2022	55.01	57.97000	99.46500	Female	0	0	0	0	0	0	0	1	0	1	0	0	0
Alexandra Feigin	2023	155.74	54.31000	100.92000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Alexei Bychenko	2017	245.96	82.87333	158.67000	Male	0	1	0	0	0	0	0	1	0	0	1	0	0
Alexei Bychenko	2018	258.28	82.85250	165.88750	Male	0	1	0	1	0	0	0	1	0	1	0	0	0
Alexei Bychenko	2019	216.60	75.77333	130.99333	Male	1	0	0	0	1	0	0	1	0	0	0	0	0
Alexia Paganini	2018	149.66	55.10500	103.83500	Female	0	0	0	0	0	0	0	1	0	1	0	0	0
Alexia Paganini	2019	50.51	61.98333	110.98667	Female	0	0	0	1	0	0	0	1	0	0	1	0	0
Alexia Paganini	2022	170.02	61.69000	111.81500	Female	0	0	0	0	0	0	0	1	0	1	0	0	0
Alina Zagitova	2018	207.72	74.27200	151.48600	Female	0	0	0	1	0	1	0	1	0	1	0	0	1
Alina Zagitova	2019	237.50	75.65250	140.12500	Female	0	0	0	0	1	1	0	1	0	0	1	0	0
Alysa Liu	2022	211.19	70.28333	135.84333	Female	0	1	0	0	0	0	1	0	0	1	0	0	0
Amber Glenn	2023	188.33	63.36333	123.12667	Female	1	1	0	0	0	0	0	0	1	0	0	0	0
Amy Lin	2017	51.86	45.40000	79.62000	Female	0	0	0	0	0	0	0	0	1	0	0	0	0
Amy Lin	2018	49.31	51.14000	86.26000	Female	0	0	0	0	0	0	0	0	1	0	0	0	0
Anastasia Gracheva	2023	50.55	39.08000	0.00000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Anastasiia Gubanova	2022	196.61	66.21000	128.36500	Female	0	0	0	0	0	0	0	1	0	1	0	0	0
Anastasiia Gubanova	2023	184.92	64.22000	122.31000	Female	0	0	1	0	1	0	0	1	0	0	0	0	0
Anastasiya Galustyan	2017	153.47	56.41667	100.21333	Female	0	0	0	1	0	0	0	1	0	0	1	0	0
Anastasiya Galustyan	2019	47.75	48.38000	84.25000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Andreas Nordebäck	2023	223.52	75.98000	136.97000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Andrei Lazukin	2019	248.74	72.49500	144.50500	Male	0	0	0	0	1	0	0	0	0	0	1	0	0
Anete Lace	2022	44.60	49.75000	0.00000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Angelina Kuchvalska	2017	155.02	50.38000	91.51667	Female	1	0	0	0	0	0	0	1	0	0	1	0	0
Anita Östlund	2018	48.99	52.59000	89.10000	Female	0	0	0	0	0	0	0	1	0	1	0	0	0
Anita Östlund	2019	53.07	52.76000	91.90000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Anna Khnychenkova	2017	46.98	48.93000	87.64000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Anna Pogorilaya	2017	183.37	73.29250	140.19000	Female	0	1	0	0	0	1	0	1	0	0	1	0	0
Anne Line Gjersem	2017	46.99	48.06000	80.62000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Anne Line Gjersem	2018	45.25	48.70000	93.98000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Antonina Dubinina	2018	41.40	36.69000	0.00000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Ashley Wagner	2017	193.54	66.93000	121.98000	Female	1	0	0	0	0	0	0	0	0	0	0	0	1
Boyang Jin	2017	303.58	86.81000	176.90000	Male	1	0	0	0	0	0	0	0	1	0	0	0	1
Boyang Jin	2018	223.41	93.83750	183.47000	Male	1	0	0	0	0	0	0	0	1	1	0	0	1
Boyang Jin	2019	262.71	85.85000	150.71000	Male	0	0	0	1	1	0	0	0	1	0	0	0	0
Boyang Jin	2023	204.22	85.32000	142.15000	Male	0	0	0	0	0	0	0	0	1	0	0	0	0
Bradie Tennell	2018	199.89	65.51000	132.71500	Female	1	0	0	0	0	0	0	0	0	1	0	0	0
Bradie Tennell	2019	213.47	65.65667	131.92333	Female	1	0	0	1	0	0	0	0	1	0	0	0	0
Bradie Tennell	2023	184.14	62.21000	110.15000	Female	0	0	1	0	1	0	0	0	1	0	0	0	0
Brendan Kerry	2017	236.24	73.46667	139.38333	Male	1	0	0	1	0	0	0	0	1	0	0	0	0
Brendan Kerry	2018	223.85	75.27333	143.16667	Male	0	0	0	0	0	0	1	0	1	1	0	0	0
Brendan Kerry	2019	222.02	74.34000	139.69667	Male	0	0	0	0	0	0	1	0	1	0	1	0	0
Burak Demirboga	2018	65.43	61.27000	105.95000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Burak Demirboga	2019	60.79	56.95000	0.00000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Burak Demirboga	2022	52.86	67.30000	100.73000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Burak Demirboga	2023	65.73	64.33000	118.49000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Camden Pulkinen	2022	271.69	65.52667	146.32000	Male	0	1	0	0	0	0	0	0	1	0	1	0	0
Carolina Kostner	2017	196.83	72.40000	138.12000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Carolina Kostner	2018	208.88	74.69200	137.22000	Female	0	1	0	0	0	1	0	1	0	1	1	0	0
Chaeyeon Kim	2023	203.51	71.39000	131.00000	Female	0	0	0	0	0	0	0	0	1	0	0	0	0
Chafik Besseghier	2017	230.13	77.95667	147.57333	Male	0	0	0	1	0	0	0	1	0	0	1	0	0
Chih-I Tsao	2017	61.52	51.02000	118.61000	Male	0	0	0	0	0	0	0	0	1	0	0	0	0
Chih-I Tsao	2018	64.06	72.57000	122.64000	Male	0	0	0	0	0	0	0	0	1	0	0	0	0
Conrad Orzel	2023	67.65	74.29333	133.77667	Male	0	1	0	0	0	0	1	0	1	0	0	0	0
Dabin Choi	2017	191.11	55.32333	115.95000	Female	0	1	0	0	0	0	1	0	1	0	0	0	0
Dabin Choi	2018	55.30	61.32333	123.83667	Female	0	0	0	0	0	0	0	0	1	1	0	0	1
Daniel Grassl	2022	266.66	87.23500	173.51000	Male	1	0	0	0	0	0	0	1	0	1	0	1	0
Daniel Grassl	2023	244.43	83.17750	166.28000	Male	1	0	1	0	0	1	0	1	0	0	0	0	0
Daniel Samohin	2018	214.01	71.04250	146.26000	Male	1	0	0	0	0	0	0	1	0	1	1	0	0
Daniel Samohin	2019	205.28	81.23667	135.11333	Male	0	0	0	1	0	0	1	1	0	0	0	0	0
Daša Grm	2017	46.63	43.48000	0.00000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Daša Grm	2018	144.51	47.40000	89.91000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Daša Grm	2019	161.16	53.50000	93.79000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Daša Grm	2022	147.12	47.85000	0.00000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Daša Grm	2023	47.04	52.47000	90.58000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Denis Ten	2017	234.31	89.21000	180.05000	Male	0	0	0	1	0	0	0	0	0	0	0	0	0
Deniss Vasiljevs	2017	239.00	70.92333	149.97667	Male	0	1	0	0	0	0	0	1	0	0	1	0	0
Deniss Vasiljevs	2018	254.86	80.89500	154.21250	Male	0	1	0	0	0	0	0	1	0	1	1	0	0
Deniss Vasiljevs	2019	218.52	77.85333	134.93333	Male	0	1	0	1	0	0	0	1	0	0	0	0	0
Deniss Vasiljevs	2022	243.00	87.59750	169.33000	Male	0	0	0	1	0	0	0	1	0	1	0	1	0
Deniss Vasiljevs	2023	243.15	78.94333	150.51000	Male	0	0	1	0	0	0	1	1	0	0	0	0	0
Dmitri Aliev	2018	252.30	89.14750	162.01000	Male	0	1	0	0	0	0	0	1	0	1	1	0	0
Donovan Carrillo	2018	200.76	59.07000	126.84000	Male	0	0	0	0	0	0	0	0	1	0	0	0	0
Donovan Carrillo	2019	54.99	71.16000	103.54000	Male	0	0	0	0	0	0	0	0	1	0	0	0	0
Ekaterina Kurakova	2022	186.43	61.08500	127.61750	Female	1	0	0	0	0	0	0	1	0	1	1	0	0
Ekaterina Kurakova	2023	181.43	62.97333	122.36667	Female	1	0	1	0	0	0	0	1	0	0	0	0	0
Ekaterina Ryabova	2019	179.88	59.95000	103.22000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Ekaterina Ryabova	2022	188.50	62.37500	122.27750	Female	0	0	0	1	0	0	0	1	0	1	1	0	0
Eliska Brezinova	2018	153.14	52.06000	97.63000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Eliska Brezinova	2019	153.45	55.85000	110.92000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Eliska Brezinova	2022	55.07	61.96500	103.36000	Female	0	0	0	0	0	0	0	1	0	1	0	0	0
Eliska Brezinova	2023	47.29	55.89500	100.40500	Female	1	0	0	0	0	0	1	0	0	0	0	0	0
Elizabet Tursynbayeva	2017	191.99	62.28000	115.41333	Female	0	1	0	0	0	0	0	0	1	0	1	0	0
Elizabet Tursynbayeva	2018	186.85	60.38750	119.42250	Female	0	0	0	1	0	0	0	0	1	1	1	0	0
Elizabet Tursynbayeva	2019	224.76	63.67000	127.53667	Female	0	0	0	0	0	0	1	0	1	0	1	0	0
Elzbieta Kropa	2018	46.53	46.06000	87.81000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Emmi Peltonen	2017	50.74	53.52000	107.05000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Emmi Peltonen	2019	53.22	58.98000	105.39500	Female	0	0	0	0	1	0	0	1	0	0	0	0	0
Eunsoo Lim	2019	205.57	65.56000	125.71667	Female	0	1	0	0	0	0	0	0	1	0	1	0	0
Evgenia Medvedeva	2017	233.41	78.22250	146.66750	Female	0	0	0	1	0	1	1	1	0	0	0	0	0
Evgenia Medvedeva	2019	223.80	64.19000	131.17000	Female	0	0	0	1	0	0	1	0	0	0	0	0	0
Gabrielle Daleman	2017	213.52	68.48000	123.40000	Female	1	0	0	1	0	0	0	0	1	0	0	0	0
Gabrielle Daleman	2018	196.72	69.21000	116.93333	Female	1	0	0	0	0	0	0	0	0	1	0	0	1
Georgiy Reshtenko	2023	59.93	54.52000	0.00000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Graham Newberry	2017	62.04	67.79000	130.27000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Graham Newberry	2022	210.40	64.49000	0.00000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Graham Newberry	2023	61.70	67.57500	109.95500	Male	0	0	1	0	0	0	0	1	0	0	0	0	0
Haein Lee	2022	196.55	65.26000	126.35333	Female	0	0	0	1	0	0	1	0	1	0	0	0	0
Haein Lee	2023	220.94	66.04667	128.56333	Female	1	0	0	1	0	0	0	0	1	0	0	0	0
Hanul Kim	2018	170.68	57.74000	116.66500	Female	0	0	0	0	0	0	0	0	1	1	0	0	0
Helery Hälvin	2017	51.94	51.72000	94.96000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Hongyi Chen	2019	157.59	54.44000	96.06000	Female	0	0	0	0	0	0	0	0	1	0	0	0	0
Ihor Reznichenko	2017	63.88	54.81000	0.00000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Ihor Reznichenko	2018	51.70	63.96000	101.69000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Ihor Reznichenko	2019	50.15	59.99000	0.00000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Ilia Malinin	2023	288.44	83.91667	192.98333	Male	1	0	0	0	1	1	0	0	0	0	0	0	0
Isabeau Levito	2023	207.65	71.03000	135.67000	Female	1	0	1	0	0	1	0	0	1	0	0	0	0
Isadora Williams	2018	42.16	55.74000	88.44000	Female	0	0	0	0	0	0	0	0	0	1	0	0	0
Isadora Williams	2019	143.22	47.92000	90.34000	Female	0	0	0	0	0	0	0	0	1	0	0	0	0
Ivan Pavlov	2017	69.26	68.94000	133.93000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Ivan Shmuratko	2019	62.99	67.26000	111.03000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Ivan Shmuratko	2022	196.65	80.12000	130.04500	Male	0	0	0	0	0	0	0	1	0	1	0	0	0
Ivett Tóth	2017	160.77	61.49000	111.16000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Ivett Tóth	2018	136.87	51.96000	97.74500	Female	0	0	0	0	0	0	0	1	0	1	0	0	0
Ivett Tóth	2019	54.87	54.90000	105.93000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Janna Jyrkinen	2023	160.91	51.83000	113.87500	Female	0	0	0	0	1	0	0	1	0	0	0	0	0
Jari Kessler	2023	61.94	67.87000	114.96000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Jason Brown	2017	269.57	80.28333	163.95000	Male	1	1	0	0	0	0	0	0	1	0	0	0	0
Jason Brown	2019	254.15	86.48000	163.58333	Male	0	0	0	1	0	0	1	0	1	0	0	0	0
Javier Fernández	2017	301.19	96.03250	189.46000	Male	0	0	0	1	0	1	0	1	0	0	1	0	0
Javier Raya	2017	66.88	66.67000	128.87000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Jenni Saarinen	2022	55.30	57.95000	98.73000	Female	0	0	0	0	0	0	0	1	0	1	0	0	0
Jinseo Kim	2017	68.66	64.26000	130.79000	Male	0	0	0	0	0	0	0	0	1	0	0	0	0
Jorik Hendrickx	2017	214.02	79.82000	152.82667	Male	1	0	0	1	0	0	0	1	0	0	0	0	0
Josefin Taljegård	2022	163.24	56.37500	106.06000	Female	0	0	0	0	0	0	0	1	0	1	0	0	0
Joshi Helgesson	2017	52.07	50.96667	98.12333	Female	0	0	0	0	0	0	1	1	0	0	0	0	1
Júlia Láng	2023	44.26	46.33000	83.95000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Julia Sauter	2019	53.11	54.29000	98.86000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Julia Sauter	2023	165.62	54.48000	103.96000	Female	0	0	1	0	0	0	0	1	0	0	0	0	0
Julian Zhi-Jie Yee	2017	213.99	72.21000	130.46000	Male	0	0	0	0	0	0	0	0	1	0	0	0	0
Julian Zhi-Jie Yee	2018	209.03	71.01500	129.23000	Male	0	0	0	0	0	0	0	0	1	1	0	0	0
Julian Zhi-Jie Yee	2019	205.97	67.70667	119.06667	Male	1	0	0	0	0	0	0	0	1	0	1	0	0
Junhwan Cha	2019	229.26	89.52000	164.80000	Male	0	0	0	0	1	1	1	0	1	0	0	0	0
Junhwan Cha	2022	82.43	97.48750	168.24750	Male	0	1	0	0	0	0	0	0	1	1	0	1	0
Junhwan Cha	2023	296.03	86.18667	170.13000	Male	1	1	0	0	0	0	0	0	1	0	0	0	0
Kaetlyn Osmond	2017	218.13	72.57000	127.19750	Female	0	0	0	0	0	1	1	0	1	0	0	0	1
Kaetlyn Osmond	2018	223.23	75.25500	141.21000	Female	0	0	0	1	0	1	1	0	0	1	0	0	0
Kailani Craine	2017	152.94	54.70000	82.21000	Female	0	0	0	0	0	0	0	0	1	0	0	0	0
Kailani Craine	2018	154.41	54.17333	96.72000	Female	0	0	0	0	0	0	1	0	1	1	0	0	0
Kailani Craine	2019	48.82	59.42500	92.44500	Female	0	1	0	0	0	0	0	0	1	0	0	0	0
Kailani Craine	2022	161.75	53.69500	106.56000	Female	0	0	0	0	0	0	0	0	1	1	0	0	0
Kaori Sakamoto	2019	222.83	68.03500	139.41250	Female	1	0	0	0	1	1	0	0	1	0	0	0	0
Kaori Sakamoto	2022	236.09	75.85333	148.28000	Female	1	1	0	0	0	0	0	0	0	1	0	0	0
Kaori Sakamoto	2023	224.61	71.88333	132.13000	Female	1	1	0	0	0	1	0	0	0	0	0	0	0
Karen Chen	2017	199.29	57.54667	117.34000	Female	0	1	0	0	0	0	0	0	1	0	0	0	1
Karen Chen	2022	192.51	65.84000	119.94000	Female	0	0	0	1	0	0	1	0	0	1	0	0	0
Kazuki Tomono	2018	256.11	79.88000	152.05000	Male	0	1	0	0	0	0	0	0	0	0	0	0	0
Kazuki Tomono	2022	269.37	92.27333	167.15667	Male	0	0	0	0	0	0	0	0	1	0	1	1	0
Kazuki Tomono	2023	273.41	87.26500	163.03500	Male	0	1	0	1	0	0	0	0	0	0	0	0	0
Keegan Messing	2018	252.30	82.47000	153.85667	Male	0	1	0	0	0	0	1	0	0	1	0	0	0
Keegan Messing	2019	237.64	84.15500	163.24000	Male	0	0	0	0	0	1	1	0	1	0	1	0	0
Keegan Messing	2022	235.03	90.51667	161.82000	Male	0	0	0	1	0	0	1	0	0	1	0	0	0
Keegan Messing	2023	265.16	82.17000	161.60000	Male	0	0	0	0	1	0	1	0	1	0	0	0	0
Keiji Tanaka	2017	222.34	75.72333	155.45333	Male	0	1	0	0	0	0	0	0	1	0	1	0	0
Keiji Tanaka	2018	236.66	85.97333	164.79667	Male	0	0	0	0	0	0	0	0	1	1	0	0	1
Keiji Tanaka	2019	238.40	81.29333	143.60000	Male	0	0	0	1	1	0	0	0	1	0	0	0	0
Kerstin Frank	2017	50.54	51.47000	80.61000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Kévin Aymoz	2019	247.47	82.61667	153.24667	Male	0	0	0	1	0	0	1	1	0	0	0	0	0
Kévin Aymoz	2022	245.46	73.87750	165.90667	Male	1	0	0	1	0	0	0	1	0	1	0	0	0
Kévin Aymoz	2023	282.97	86.35500	161.95000	Male	0	0	0	0	1	0	0	1	0	0	0	0	0
Kevin Reynolds	2017	253.84	78.46500	155.22000	Male	0	0	0	0	0	0	1	0	1	0	0	0	0
Kimmy Repond	2023	194.09	63.83000	128.68000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Kyarha Van Tiel	2019	41.85	44.00000	0.00000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Kyrylo Marsak	2023	68.60	70.41000	111.57000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Lara Naki Gutmann	2022	164.39	53.88500	107.39500	Female	0	0	0	0	0	0	0	1	0	0	0	1	0
Lara Naki Gutmann	2023	178.43	55.39000	113.90000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Larkyn Austman	2018	50.17	46.60500	81.77000	Female	0	0	0	0	0	0	1	0	0	1	0	0	0
Larry Loupolover	2017	38.97	51.30000	0.00000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Larry Loupolover	2018	61.82	52.44000	0.00000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Laurine Lecavelier	2017	162.99	65.21000	121.16500	Female	0	0	0	1	0	0	0	1	0	0	0	0	0
Laurine Lecavelier	2018	173.23	58.37333	99.92333	Female	0	0	0	1	0	0	1	1	0	0	0	0	0
Laurine Lecavelier	2019	170.59	58.17333	111.72667	Female	1	0	0	1	0	0	0	1	0	0	0	0	0
Léa Serna	2022	54.30	62.45500	108.21000	Female	0	0	0	1	0	0	0	1	0	0	0	0	0
Lindsay Van Zundert	2022	171.39	54.08000	116.57000	Female	0	0	0	0	0	0	0	1	0	1	0	0	0
Lindsay Van Zundert	2023	159.55	56.15333	101.56333	Female	0	0	0	1	0	0	1	1	0	0	0	0	0
Loena Hendrickx	2017	172.82	55.41000	117.30000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Loena Hendrickx	2018	192.31	55.14500	119.25000	Female	0	0	0	0	0	0	0	1	0	1	0	0	0
Loena Hendrickx	2019	186.29	58.65000	128.05000	Female	1	0	0	0	1	0	0	0	0	0	0	0	0
Loena Hendrickx	2022	217.70	71.07500	138.30000	Female	0	0	0	0	0	0	0	1	0	1	1	1	0
Loena Hendrickx	2023	210.42	72.43000	130.09000	Female	0	0	0	1	1	1	0	1	0	0	0	0	0
Luc Maierhofer	2019	65.78	63.63000	125.37000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Lukas Britschgi	2019	54.58	55.86000	0.00000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Lukas Britschgi	2023	257.34	72.62000	155.14667	Male	0	0	0	1	0	0	1	1	0	0	0	0	0
Madeline Schizas	2022	188.14	63.65333	121.10000	Female	0	0	0	0	0	0	1	0	0	1	1	0	0
Madeline Schizas	2023	187.49	64.40000	111.65333	Female	0	0	0	0	1	0	1	0	1	0	0	0	0
Mai Mihara	2017	197.88	66.91333	126.77000	Female	1	0	0	0	0	0	0	0	1	0	0	0	1
Mai Mihara	2023	205.70	73.46333	136.45000	Female	0	0	1	0	1	1	0	0	0	0	0	0	0
Mana Kawabe	2022	182.44	63.29000	122.94000	Female	0	1	0	0	0	0	1	0	0	1	0	0	0
Maria Sotskova	2017	192.20	69.14500	127.74000	Female	0	1	0	1	0	1	0	1	0	0	0	0	0
Maria Sotskova	2018	196.61	68.09000	135.20800	Female	0	0	0	1	0	1	1	1	0	1	0	0	0
Mariah Bell	2017	187.23	61.06500	123.28000	Female	1	0	0	0	0	0	0	0	1	0	0	0	0
Mariah Bell	2018	174.40	61.33333	118.81333	Female	0	1	0	0	0	0	0	0	1	0	1	0	0
Mariah Bell	2019	208.07	65.44667	128.93667	Female	0	1	0	0	0	0	1	0	1	0	0	0	0
Mariah Bell	2022	208.66	65.18667	135.96000	Female	0	0	0	1	0	0	0	0	0	1	1	0	0
Marilena Kitromilis	2022	53.32	44.03000	0.00000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Marilena Kitromilis	2023	48.92	49.86000	97.33500	Female	1	0	0	0	0	0	0	1	0	0	0	0	0
Mark Gorodnitsky	2023	232.13	64.94000	137.40000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Matteo Rizzo	2018	225.44	76.94500	148.97500	Male	0	0	0	0	0	0	0	1	0	1	0	0	0
Matteo Rizzo	2019	257.66	78.83333	153.70000	Male	1	1	0	0	0	0	0	1	0	0	0	0	0
Matteo Rizzo	2022	255.75	83.62000	167.66000	Male	0	1	0	0	0	0	0	0	0	1	1	0	0
Matteo Rizzo	2023	256.04	82.07000	168.50000	Male	0	1	0	0	0	0	1	1	0	0	0	0	0
Maurizio Zandrón	2022	228.27	70.75000	123.16000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Maurizio Zandrón	2023	194.31	70.39000	134.31000	Male	0	1	0	0	0	0	0	1	0	0	0	0	0
Max Aaron	2018	241.49	81.96667	168.81333	Male	0	0	0	1	0	0	0	0	1	0	0	0	1
Maxim Kovtun	2017	245.84	77.35333	162.30667	Male	1	0	0	0	0	0	0	1	0	0	0	0	1
Mia Caroline Risa Gomez	2023	43.54	49.14000	88.48000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Michael Christian Martinez	2017	196.79	72.47000	141.68000	Male	0	0	0	0	0	0	0	0	1	0	0	0	0
Michaela Hanzlikova	2017	32.21	52.39000	85.84000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Michal Březina	2017	226.26	74.94333	143.29333	Male	0	0	0	0	0	0	1	1	0	0	0	0	1
Michal Březina	2018	243.99	78.61250	153.57750	Male	0	1	0	0	0	0	1	1	0	1	0	0	0
Michal Březina	2019	254.28	87.06750	159.68250	Male	1	0	0	0	1	1	0	1	0	0	0	0	0
Mihhail Selevko	2023	230.94	76.29667	131.37667	Male	1	0	0	1	0	0	0	1	0	0	0	0	0
Mikhail Kolyada	2017	257.47	84.14000	156.25000	Male	0	1	0	0	0	0	0	1	0	0	1	0	0
Mikhail Kolyada	2018	272.32	91.64800	179.47000	Male	0	0	0	0	0	1	0	1	0	1	1	0	1
Mikhail Kolyada	2019	262.44	83.78333	151.24333	Male	0	0	0	0	1	0	0	1	0	0	1	0	0
Mikhail Shaidorov	2023	236.93	72.43000	164.71000	Male	0	0	0	0	0	0	0	0	1	0	0	0	0
Mirai Nagasu	2018	187.52	62.75000	123.66667	Female	0	1	0	0	0	0	0	0	0	1	1	0	0
Misha Ge	2017	243.45	75.54667	155.96667	Male	0	0	0	1	0	0	1	0	1	0	0	0	0
Misha Ge	2018	249.57	84.15000	167.74250	Male	0	0	0	1	0	0	0	0	1	1	1	0	0
Morisi Kvitelashvili	2017	239.24	76.85000	161.35000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Morisi Kvitelashvili	2018	67.01	80.23750	146.21250	Male	0	0	0	1	0	0	0	1	0	1	1	0	0
Morisi Kvitelashvili	2019	240.74	77.18667	147.31000	Male	1	0	0	0	0	0	0	1	0	0	1	0	0
Morisi Kvitelashvili	2022	272.03	89.42750	166.00500	Male	0	0	0	0	0	0	1	1	0	1	1	0	0
Morisi Kvitelashvili	2023	212.32	63.13000	132.41667	Male	0	0	1	0	1	0	0	1	0	0	0	0	0
Nam Nguyen	2018	67.79	76.88333	153.27667	Male	0	1	0	0	0	0	0	0	1	0	1	0	0
Nam Nguyen	2019	237.27	77.21000	146.26333	Male	1	0	0	0	0	0	1	0	1	0	0	0	0
Natasha Mckay	2017	50.10	45.97000	94.88000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Natasha Mckay	2018	45.89	45.12000	0.00000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Natasha Mckay	2019	151.56	48.20000	91.88000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Natasha Mckay	2022	159.27	54.80500	104.67000	Female	0	0	0	0	0	0	0	1	0	1	0	0	0
Nathan Chen	2017	290.72	92.30250	188.70250	Male	0	1	0	1	0	1	0	0	1	0	0	0	0
Nathan Chen	2018	321.40	97.56250	190.82000	Male	1	0	0	0	0	1	0	0	0	1	1	0	0
Nathan Chen	2019	323.42	90.17000	188.02000	Male	1	0	0	1	0	1	0	0	0	0	0	0	0
Nicholas Vrdoljak	2017	57.28	53.45000	0.00000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Nicholas Vrdoljak	2018	59.74	58.30000	0.00000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Nicole Rajičová	2017	165.55	57.44000	111.54667	Female	0	1	0	0	0	0	0	1	0	0	1	0	0
Nicole Rajičová	2018	49.87	57.59750	111.02250	Female	1	1	0	0	0	0	0	1	0	1	0	0	0
Nicole Rajičová	2019	51.22	64.08000	104.95000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Nicole Schott	2017	161.41	56.88000	103.75000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Nicole Schott	2018	174.13	54.66500	112.18750	Female	0	0	0	1	0	0	0	1	0	1	1	0	0
Nicole Schott	2019	170.56	50.68000	98.58000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Nicole Schott	2022	188.42	60.64500	111.20500	Female	0	1	0	0	0	0	0	1	0	1	0	1	0
Nicole Schott	2023	197.76	57.06000	111.46667	Female	1	0	1	0	0	0	0	1	0	0	0	0	0
Niina Petrõkina	2022	176.60	58.30000	128.77000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Niina Petrõkina	2023	193.49	60.51333	121.27667	Female	0	1	0	0	0	0	1	1	0	0	0	0	0
Nika Egadze	2023	65.17	79.95667	149.01333	Male	0	1	0	1	0	0	0	1	0	0	0	0	0
Nikita Starostin	2022	205.72	72.12000	142.28000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Nikita Starostin	2023	217.87	74.70000	123.27000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Nikolaj Majorov	2022	216.45	78.54000	142.24000	Male	0	0	0	0	0	0	0	0	0	1	0	0	0
Nina Pinzarrone	2023	191.78	61.35000	124.57000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Olga Mikutina	2022	182.98	59.46333	109.63667	Female	0	0	0	0	0	0	0	1	0	1	1	0	0
Olga Mikutina	2023	172.31	58.57667	105.56667	Female	0	1	0	1	0	0	0	1	0	0	0	0	0
Patrick Chan	2017	295.16	90.54750	179.80250	Male	0	0	0	0	0	1	1	0	1	0	0	0	1
Paul Fentz	2017	217.91	72.68000	153.17000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Paul Fentz	2018	230.92	71.91667	131.79000	Male	0	0	0	0	0	0	1	1	0	1	0	0	0
Paul Fentz	2019	63.24	73.99000	141.27500	Male	0	0	0	0	0	0	0	1	0	0	1	0	0
Pernille Sørensen	2019	54.36	50.59000	81.19000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Phillip Harris	2018	187.69	67.77000	140.45000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Rika Hongo	2017	169.83	62.84667	110.60667	Female	0	0	0	0	0	0	1	0	1	0	0	0	1
Rika Kihira	2019	223.49	72.14750	149.18750	Female	0	1	0	1	0	1	0	0	1	0	0	0	0
Rinka Watanabe	2023	192.81	64.95250	130.59000	Female	0	1	0	0	0	1	1	0	1	0	0	0	0
Romain Ponsart	2018	229.20	62.63000	136.79000	Male	0	0	0	1	0	0	0	1	0	0	0	0	0
Roman Sadovsky	2022	245.36	73.43333	157.00000	Male	0	0	0	0	0	0	1	0	0	1	1	0	0
Satoko Miyahara	2018	210.08	71.61200	138.13200	Female	1	1	0	0	0	1	0	0	1	1	0	0	0
Satoko Miyahara	2019	215.95	72.48667	141.01000	Female	1	1	0	0	0	1	0	0	0	0	0	0	0
Shoma Uno	2017	319.31	93.71000	190.03250	Male	1	0	0	0	0	1	0	0	1	0	1	0	0
Shoma Uno	2018	273.77	100.74200	192.31200	Male	0	0	0	1	0	1	1	0	1	1	0	0	0
Shoma Uno	2019	270.32	91.19750	188.28250	Male	0	1	0	0	0	1	1	0	1	0	0	0	0
Shoma Uno	2022	312.48	99.18333	185.42667	Male	1	1	0	0	0	0	0	0	0	1	0	0	0
Shoma Uno	2023	301.14	93.87667	191.91333	Male	0	1	0	0	0	1	1	0	0	0	0	0	0
Shuran Yu	2017	52.87	43.26000	75.14000	Female	0	0	0	0	0	0	0	0	1	0	0	0	0
Sihyeong Lee	2022	225.06	72.41000	144.05000	Male	0	0	0	0	0	0	0	0	1	1	0	0	0
Slavik Hayrapetyan	2017	57.14	60.69000	120.09000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Slavik Hayrapetyan	2018	199.72	69.49000	127.14000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Slavik Hayrapetyan	2019	60.66	59.87000	0.00000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Sofia Samodurova	2019	208.58	68.23250	135.48750	Female	1	0	0	0	0	1	0	1	0	0	1	0	0
Sofja Stepchenko	2023	158.38	55.32000	104.02000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Sophia Schaller	2019	48.72	44.20000	0.00000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Sota Yamamoto	2023	232.39	94.59000	168.77667	Male	0	1	0	1	0	1	0	0	0	0	0	0	0
Stéphane Walker	2017	64.04	62.86000	133.88000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Stéphane Walker	2018	65.79	65.96000	119.45000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Tomás Guarino Sabaté	2022	208.95	66.20000	112.47000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Tomás Guarino Sabaté	2023	67.60	71.65000	133.54000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Tzu-Han Ting	2022	55.24	49.15000	96.42000	Female	0	0	0	0	0	0	0	0	1	0	0	0	0
Valentina Matos	2019	50.25	42.86000	0.00000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Valtter Virtanen	2017	59.45	56.52000	107.57000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Valtter Virtanen	2018	55.49	60.23000	121.54000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Valtter Virtanen	2019	55.73	48.16000	106.58000	Male	0	0	0	0	1	0	0	0	0	0	0	0	0
Vincent Zhou	2018	235.24	76.96000	174.89333	Male	0	0	0	1	0	0	0	0	0	1	0	0	1
Vincent Zhou	2019	281.16	84.15333	156.31000	Male	1	1	0	0	0	0	0	0	1	0	0	0	0
Vincent Zhou	2022	277.38	98.47000	179.65500	Male	1	1	0	0	0	0	0	0	0	0	0	0	0
Viveca Lindfors	2018	166.23	51.62000	96.27000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Vladimir Litvintsev	2019	230.84	73.60000	130.68000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Vladimir Litvintsev	2022	233.62	83.80500	158.14000	Male	0	0	0	0	0	0	0	1	0	1	0	0	0
Vladimir Samoilov	2023	61.48	78.26000	113.33000	Male	0	0	0	0	0	0	0	1	0	0	0	0	0
Wakaba Higuchi	2017	188.05	62.14333	121.83000	Female	0	1	0	1	0	0	0	0	1	0	0	0	0
Wakaba Higuchi	2018	210.90	71.13000	136.13667	Female	0	0	0	0	0	1	0	0	0	0	1	0	1
Wakaba Higuchi	2022	188.15	68.93000	139.27667	Female	0	0	0	1	0	0	1	0	0	1	0	0	0
Xiangning Li	2017	175.37	55.14000	105.92000	Female	0	0	0	0	0	0	0	0	1	0	0	0	1
Xiangning Li	2018	50.06	55.97750	107.51500	Female	1	0	0	0	0	0	0	0	1	1	0	0	1
Yasmine Kimiko Yamada	2017	47.86	42.33000	0.00000	Female	0	0	0	0	0	0	0	1	0	0	0	0	0
Yelim Kim	2023	174.30	68.88500	128.39500	Female	0	1	0	1	0	1	0	0	1	0	0	0	0
Yi Christy Leung	2019	177.22	53.93000	110.86000	Female	0	0	0	0	0	0	0	0	1	0	0	0	0
Young You	2022	204.91	69.25250	138.80250	Female	1	1	0	0	0	0	0	0	1	1	0	0	0
Yuma Kagiyama	2022	297.60	96.43000	195.06333	Male	0	0	0	1	0	0	0	0	0	1	0	1	0
Yuzuru Hanyu	2017	321.59	96.77750	193.75750	Male	0	1	0	0	0	1	1	0	1	0	0	0	0
Yuzuru Hanyu	2019	300.97	108.61000	179.16000	Male	0	0	0	0	1	0	0	0	0	0	1	0	0
Zijun Li	2017	159.80	61.86000	115.23333	Female	0	0	0	0	0	0	0	0	1	0	1	0	1

Codebook

Here are the final variables I have selected to use in my dataframe for analysis:

• skater: The skater’s full name

• year: the ending year of the season that the competitions are a part of

• worldscore: the total score of the skaters in the World Championships competition

• spavg: a skater’s average short program score within that season

• fpavg: a skater’s average free program score within that season

• sex: the skater’s sex. Male or Female.

• The competition variables: the event name abbreviation. A 1 indicates that the skater attended the event that season, and a 0 indicates that they did not. The abbreviations are as follows:

  • GPUSA: Grand Prix Skate America

  • GPCAN: Grand Prix Skate Canada

  • GPFRA: Grand Prix de France

  • GPCHN: Grand Prix Cup of China

  • GPFIN: Grand Prix Espoo/Helsinki

  • GPJPN: Grand Prix NHK Trophy

  • GPGBR: Grand Prix MK John Wilson Trophy

  • GPITA: Grand Prix Gran Premio d’Italia

  • GPRUS: Gran Prix Rostelecom Cup

  • GPF: Grand Prix Final

  • EC: European Championships

  • 4CC: 4 Continents Championships

  • OLY: Olympics

EDA

Scores Across Sexes

Let’s take a deeper look into the different variables. The first thing to note is that Men’s Singles tend to have higher score than Women’s because of the difference in difficulties between the categories. We can see that reflected in the histograms below, where we see the distribution of the Worlds Total scores between the 2 sexes. We can see their distribution between the 2 sexes differ a lot, so it is important during our analysis that we differ the 2 sexes from each other.

data %>%   
  ggplot(aes(x = worldscore, color = sex)) + 
  geom_histogram(alpha = 0.5, aes(fill = sex), position = 'identity')+
  labs(title = 'Scores Between Sexes at Worlds', x = 'Total Scores')+
  theme_bw()

We can see how the data clusters between those that did both the free and short program at Worlds, and those that did not qualify to the Free Program. For those that only participated in the short program, men seems to have a peak at around a score of 60, while women peak at around 50. For those that participated in both segments, we can see the bulk of the women’s score ranging from about 150 to 200, while the bulk of the men’s ranges from about 225 to 260. There is no limit to figure skating scores, but the world record in total scores for women’s is 272.71 and for men’s is 335.3, so we will expect those to be the maximum scores for the data we have.

Average Scores Across Years and Sexes

There is also a difference in the spread of scores between years for both segments. We can see the change in distribution between years for average short program scores and average free program scores below. For the free program scores, I filtered out any missing free program scores that were filled in as 0 so that we can get a more accurate look at the differences in scoring for the free program.

data %>%
  ggplot(aes(x = year, y = spavg, group = year, color = sex)) +
  geom_boxplot(fill = 'grey')+
  facet_wrap(~sex)+
  labs(title = 'Distribution of Avg. SP Score Between Years and Sexes', y = 'SP Score Averages', x = 'Year')+
  theme_bw()

data %>%
  filter(fpavg != 0) %>% 
  ggplot(aes(x = year, y = fpavg, group = year, color = sex)) +
  geom_boxplot(fill = 'grey')+
  facet_wrap(~sex)+
  labs(title = 'Distribution of Avg. FS Score Between Years and Sexes', y = 'FS Score Averages', x = 'Year')+
  theme_bw()

Figure skating has a lot of rule changes throughout the years, and these rule changes affect how a skater is able to earn points. Sometimes it might be easier for them to earn a few extra points or sometimes it could be harder to get an extra point boost over other skaters. These changes can be reflected in how the distribution of scores vary throughout the years, and it is why we should look at how the year affects a skater’s score. We can also see that average free program score varies more throughout the years compared to short program scores. The short programs have more rigorous rules that restricts a skater’s score-earning potential and the free program is when it is more open to point-earning but also the most affected by rule changes.

Number of Competitors

Different competitions have different amounts of competitors and ways to qualify for them. The 6 Grand Prix events are invite-only, usually allowing 10 to 12 competitors for each for Men’s and Women’s. A skater can be invited to 1 or 2 of these events. On top of those 6 events, the Grand Prix Final has the top 6 skaters for each gender in the GP events compete against each other. The 4 Continents, European Championships, and Olympics allow each ISU-recognized nation to have at least 1 skater enter the competition. A nation could earn up to 3 spots based on previous performances at other events. A skater also needs to have a minimum technical score (a component of their score for a program) to enter. Because of all of these rules, these 3 events can have varying amounts of competitors in them. We can see this broken down below with the number of competitors in each competition for the 2022 season.

reg_merged %>% 
  filter(year == 2022) %>% 
  ggplot(aes(y= competition))+
  geom_bar(aes(fill = sex))+
  facet_wrap(~sex)+
  labs(title = 'Number of Competitors')+
  theme_bw()

Scores Across Competitions

As mentioned before, there is a discrepancy with scoring between different competitions. Let’s take a look at the scores from the 2022-2023 season so we can see how the spread and median of scores differ.

reg_merged %>% 
  filter(year == 2023) %>% 
  ggplot(aes(x = competition, y = total_score, color = sex)) +
  geom_boxplot(fill = 'grey')+
  geom_jitter(alpha = 0.4)+
  facet_wrap(~sex)+
  labs(title = 'Distribution of Score across 2022-23 Competitions', x = 'Competition', y = 'Total Score')+
  theme_bw()+
  theme(axis.text.x=element_text(angle=90,hjust=1))

All the competitions have differing medians and ranges of scores. A popular fan opinion for this season was that GP Japan was relatively easy on the scoring, so a lot of mistakes weren’t called to deduct from a skater’s points. This can be reflected with a higher median score compared to the other GP events across both sexes.

Average Total Score vs. World Score

It is also important to understand that average total score is not a good predictor of a skater’s score at the World Championship. The graphs below show World Score on the x-axis and average Total Score on the y-axis by year and sex. If average Total Score was the sole predictor of World Score, we would see the graphs with only positive sloping lines; however, that is not the case. Someone with a higher average score could score lower than someone with a lower average score. That is why we need to look at other variables that could affect a skater’s scores throughout the season and at Worlds.

graphdata %>% 
  ggplot(aes(x = worldscore)) +
  geom_line(aes(y=tsavg, color = sex))+
  labs(x = 'World Score', y = 'Average Total Score', title = 'Average Total Score vs. World Score')+
  facet_wrap(~year)+
  theme_bw()

Setting Up Models

Data Splitting

Our first step before fitting any models is to split our data into 2 sets, the training and testing set. The training set we will use to fit our model onto and the testing set is used to see how well our models will perform with unseen data. I chose a 70/30 split, where 70% of the data will go into the training set and 30% into the testing set. I also stratified the split on the response variable, worldscore, so that we get an even distribution of the variable in both sets, which is especially useful since we have that big gap in scores between the 24th and 25th place at Worlds, as mentioned before.

set.seed(1012) # for reproducibility

# 70/30 split, stratify on world_rank
scores_split <- initial_split(data, prop = 0.7, strata = worldscore)

score_train <- training(scores_split)
score_test <- testing(scores_split)

Let’s check the dimensions for the training dataset:

dim(score_train)

[1] 223  19

And the testing dataset:

dim(score_test)

[1] 96 19

The training set has 223 observations while the testing set has 96. This looks about right for our 70/30 split.

Building Recipes

Now we will build a recipe with our predictor and response variables to use for the models we will create. We can make one universal recipe, since we will use the same predictors and response for each model. We want to use all of the variables in our dataset, except for the skater’s name, and have worldscore be our response variable. We also want to make sure we turn any nominal variables into dummy variables, which indicates the presence or absence of a certain level of the nominal variable with either a 0 or 1. Lastly, we will also normalize our data, since the ranges of spavg and fpavg vary. Below is our recipe:

# Create Recipe
score_recipe <- recipe(worldscore ~ ., data = score_train) %>% 
  step_rm('skater') %>% 
  step_dummy(all_nominal_predictors()) %>% 
  step_normalize()

K-Fold Cross Validation

Next, we will prepare our data for k-fold cross validation. K-fold cross validation will split our training data into k subset or folds. One fold will be set aside, while the models we create will be fit on to the remaining k-1 folds. Then the fitted models will be tested on the fold that was set aside. We will repeat these steps k times, making sure to have a different fold be set aside each time. In the end, we will take the average of the model performance metrics from the testing fold to see how well the model performs on the training data.

K-fold cross validation allows a more accurate evaluation of a model’s performance since we are fitting and evaluating the model on multiple different splits. We measure multiple performance metrics and take an average rather than just measuring one metric.

For our analysis, we will use 10 folds. Below, we are splitting our training data into 10 folds while also stratifying on our response variable, worldscore, to make sure there is a balanced distribution of the variable between the different folds.

score_folds <- vfold_cv(score_train, v = 10, strata = worldscore)

Model Fitting

Now we will build our model and fit them onto the data. I will be fitting 5 different types of models: K-nearest neighbors, linear regression, elastic net, random forest, and gradient-boosted tree. For each model, we will be following these steps:

1. Set up the model, tuning for any parameters that are needed. Add the engine and specify regression mode if needed.

2. Set up the workflow, adding the model and recipe.

3. Create tuning grid if there are any tuning parameters. Specify the range and levels of these parameters.

4. Tune the model with the workflow, cross-validation flows, and the tuning grid.

5. Save the tuned models to a RDS file and later, load back in the saved files. This will help us save time from continuously rerunning any time-consuming models.

K-Nearest Neighbors

K-nearest neighbors is a non-parametric model that measures the distance between data points to make predictions. It selects k nearest data points to the new data point we are trying to predict and averages the data points to predict the outcome of the new data. To choose k, we will tune it and choose the best performing k from a range of 1 to 10 with 10 levels.

##K-nearest neighbors

#set up model, tuning for neighbors parameter
knn_model <- nearest_neighbor(neighbors = tune()) %>% 
  set_mode('regression') %>% 
  set_engine('kknn')

#set up workflow and add model and recipe
knn_workflow <- workflow() %>% 
  add_model(knn_model) %>% 
  add_recipe(score_recipe)

#set up grid
knn_grid <- grid_regular(neighbors(range = c(1, 10)), levels = 10)

#tune model
knn_tune <- tune_grid(
  object = knn_workflow,
  resamples = score_folds,
  grid = knn_grid
)

#save models
write_rds(knn_tune, file = 'tuned_models/knn.rds')

Linear Regression

Linear regression is one of the most simple models, creating a linear relationship between the response variable and the predictor variables. It is not very flexible but it is easy to use and understand. There are no parameters to tune, so after setting up our workflow, we can skip right into fitting the model onto the folds.

##Linear regression

#set up model
lm_model <- linear_reg() %>% 
  set_engine('lm')

#set up workflow
lm_workflow <- workflow() %>% 
  add_model(lm_model) %>% 
  add_recipe(score_recipe)

#no tuning for linear regression, so we will skip into fitting the model onto the cross validation folds
lm <- fit_resamples(lm_workflow, resamples = score_folds)

Elastic Net

Elastic Net combines the regularization techniques of Lasso regression (shrinking irrelevant variables to 0) and Ridge regression (shrinking some variables to smaller numbers but not 0). This will help us get rid of irrelevant variables and also fix any multicollinearity problems. The model has 2 parameters: mixture which tells us what proportion of Lasso regularization is used in the model (0 for only Ridge regression, 1 for only Lasso), and penalty which tells us the overall level of regularization in the model. We will tune these 2 parameters on a range of 0 to 1 for mixture and 0 to 3 for penalty, each with 10 levels.

##Elastic net

#set up model, tuning for penalty and mixture
en_model <- linear_reg(mixture = tune(), penalty = tune()) %>%
  set_engine("glmnet") %>% 
  set_mode("regression")

#set up workflow
en_workflow <- workflow() %>%
  add_model(en_model) %>% 
  add_recipe(score_recipe)

#set up grid
en_grid <- grid_regular(penalty(range = c(0,3), trans = identity_trans()), mixture(range = c(0,1)), levels = 10)

#tune model
en_tune <- tune_grid(
  object = en_workflow,
  resamples = score_folds,
  grid = en_grid
)

#save model
write_rds(en_tune, file = 'tuned_models/en.rds')

Random Forest

A Random Forest model creates multiple decision trees that are each trained on a random subset of training data with a random subset of predictors. The predictions of all the trees are averaged to make predictions on new data. The model was 3 parameters: mtry which tells us the number of predictors that are randomly sampled at each split, trees which tells us the number of trees we want to create in the Random Forest, and min_n which tells us the minimum number of observations in the data sample that is required for the tree to continue splitting. We are tuning these parameters, with mtry allowed a range of 1 to 17 (the total number of predictors we have), trees gets a range of 200 to 500, and min_n gets a range of 8 to 13.

## Random Forest

#set up model, tuning for mtry, trees, min_n
rf_model <- rand_forest(mtry = tune(),
                        trees = tune(),
                        min_n = tune()) %>% 
  set_engine('ranger', importance = 'impurity') %>% 
  set_mode('regression')

#set up workflow
rf_flow <- workflow() %>% 
  add_model(rf_model) %>% 
  add_recipe(score_recipe)

#set up grid
#rf_grid <- grid_regular(mtry(range = c(1, 17)),
                        #trees(range = c(200, 500)),
                        #min_n(range = c(8, 13)), levels = 10)

#tune model
#rf_tune <- tune_grid(
  #object = rf_flow,
  #resamples = score_folds,
  #grid = rf_grid)

#save model
#write_rds(rf_tune, file = 'tuned_models/rf.rds')

Gradient-Boosted Tree

Gradient-Boosted Trees sequentially build trees based on the residuals of a model, rather than the outcome variable. It adds new trees to update the residuals of the previous trees so that it eventually minimizes overall error. We will again tune the parameters mtry and trees with the same range, but this time we will tune learn_rate rather than min_n since it has a bigger impact on the performance of the model. We gave learn_rate a range of 0.01 to 0.1 with 10 levels for each of the parameters, which tells us the rate that the boosted tree will learn.

##Gradient-boosted tree

#set up model, tuning for mtry, tress, learn_rate
gbt_model <- boost_tree(mtry = tune(),
                        trees = tune(),
                        learn_rate = tune()) %>% 
  set_engine('xgboost') %>% 
  set_mode('regression')

#set up workflow
gbt_flow <- workflow() %>% 
  add_model(gbt_model) %>% 
  add_recipe(score_recipe)

#set up grid
gbt_grid <- grid_regular(mtry(range = c(1, 17)),
                         trees(range = c(200, 500)),
                         learn_rate(range = c(0.01, 0.1)),
                         levels = 10)

#tune model
gbt_tune <- tune_grid(
  object = gbt_flow,
  resamples = score_folds,
  grid = gbt_grid
)

#save model
write_rds(gbt_tune, file = 'tuned_models/gbt.rds')

Model Results

With all of our models competed and saved, lets load back in the saved results and analyze their performance.

#K Nearest Neighbors
knn <- read_rds(file = 'tuned_models/knn.rds')

#Linear model saved under lm variable

#Elastic Net
en <- read_rds(file = 'tuned_models/en.rds')

#Random Forest
rf <- read_rds(file = 'tuned_models/rf.rds')

#Boosted Trees
bt <- read_rds(file = 'tuned_models/gbt.rds')

To analyze the performances of the models, I will use Root Mean Square Error (RMSE) as my performance metric. RMSE is a common measure of a regression model’s performance, showing us how far away the model’s predicted value is from the data’s true value. A better performing model will have a lower RMSE, telling us that the predicted values are closer to the true values. The RMSE value we will be comparing is the average RMSE across the our cross-validation folds. We will begin by comparing the models we tuned and see which values for the parameters we tuned worked the best based on RMSE.

Best Parameters

K-Nearest Neighbors

Let’s take a look at which K-Nearest Neighbors model performed the best. Below, we can see a graph of the RMSE for each k value. The best performing k value is the one with the lowest RMSE, which looks to be k = 10

autoplot(knn, metric = 'rmse')

Elastic Net

Next, let’s take a look at the RMSE for the Elastic Net model. The x-axis shows the penalty, the different colored lines show the mixture, and the y-axis shows the RMSE. In general, when the penalty and mixture is higher, the RMSE is lower and the model performs better.

autoplot(en, metric = 'rmse')

Random Forest

Now, let’s look at the RMSE for the Random Forest model. The x-axis shows the mtry, the different colored lines show the number of trees, and the y-axis shows the RMSE. In general, when the value for mtry was higher, the RMSE is lower and the model performs better.

autoplot(rf, metric = 'rmse')

Gradient-Boosted Tree

Finally, let’s take a look at the RMSE for the Gradient-Boosted Tree model. The x-axis shows the mtry, the different colored lines show the number of trees, the different graphs show the learning rate, and the y-axis shows the RMSE. Across the parameters, there doesn’t seem to be a pattern of which level of the parameters performed the best.

autoplot(bt, metric = 'rmse')

Best Models

Now that we get a general sense of which parameters worked best, let’s compare the different types of models with each other. We will use the function show_best to choose the best model based on the lowest RMSE value. Below are the tables showing the best performing models for each type. The variable mean is the mean RMSE value across the cross-validation folds, and the variable std_err is the standard deviation of the RMSE values.

#K-nearest Neighbors
knn_best <- show_best(knn, metric = 'rmse', n =1)

knn_best %>%
  kable(caption = 'Best K-nearest Neighbors Model') %>% 
  kable_styling(full_width = F, position = "left")

Best K-nearest Neighbors Model

neighbors	.metric	.estimator	mean	n	std_err	.config
10	rmse	standard	61.95858	10	1.856955	Preprocessor1_Model10

#Linear Regression
lm_best <- collect_metrics(lm)[1,]

lm_best %>%
  kable(caption = 'Linear Model') %>% 
  kable_styling(full_width = F, position = "left")

Linear Model

.metric	.estimator	mean	n	std_err	.config
rmse	standard	51.75774	10	2.882907	Preprocessor1_Model1

#Elastic Net
en_best <- show_best(en, metric = 'rmse', n =1)

en_best %>%
  kable(caption = 'Best Elastic Net Model') %>% 
  kable_styling(full_width = F, position = "left")

Best Elastic Net Model

penalty	mixture	.metric	.estimator	mean	n	std_err	.config
3	1	rmse	standard	52.52107	10	1.912929	Preprocessor1_Model100

#Random Forest
rf_best <- show_best(rf, metric = 'rmse', n =1)

rf_best %>%
  kable(caption = 'Best Random Forest Model') %>% 
  kable_styling(full_width = F, position = "left")

Best Random Forest Model

mtry	trees	min_n	.metric	.estimator	mean	n	std_err	.config
8	266	13	rmse	standard	49.63054	10	2.311282	Preprocessor1_Model525

#Boosted Tree
bt_best <- show_best(bt, metric = 'rmse', n =1)

bt_best %>%
  kable(caption = 'Best Gradient-Boosted Tree Model') %>% 
  kable_styling(full_width = F, position = "left")

Best Gradient-Boosted Tree Model

mtry	trees	learn_rate	.metric	.estimator	mean	n	std_err	.config
11	500	1.096478	rmse	standard	59.77649	10	2.639742	Preprocessor1_Model0640

We can also compare the other models visually by plotting their mean RMSE values and the standard deviation.The graph below for the mean RMSE and the standard deviation for the models on the y-axis and the different models on the x-axis.

#combine RMSE and standard deviation for each model into a dataframe
rmse <- rbind(knn_best %>% select(mean, std_err) %>% mutate(model = 'KNN'), 
              en_best%>% select(mean, std_err)%>% mutate(model = 'Elastic Net'),
              rf_best%>% select(mean, std_err)%>% mutate(model = 'Random Forest'),
              bt_best%>% select(mean, std_err)%>% mutate(model = 'Boosted Tree'),
              lm_best%>% select(mean, std_err)%>% mutate(model = 'Linear Reg.'))

rmse %>% 
  ggplot(aes(x = model, y = mean, ymin = mean - std_err, ymax = mean + std_err)) +
  geom_pointrange()+
  labs(title = 'RMSE and Standard Deviation for Each Model', x = 'Model', y = 'RMSE')+
  theme_bw()

The model with that performs the best is the Random Forest model, with the lowest mean RMSE and the lowest standard deviation. The Linear Regression model performs the next best, then Elastic Net, then the Gradient-Boosted Tree model, and finally the K-Nearest Neighbors model.

Best Model Results

Here is the best performing model with the values of the parameters. It is a Random Forest model with a mtry value of 8, 266 trees, and a min_n value of 13. The RMSE value for this model is about 49.63.

rf_best%>%
  kable(caption = 'Best Random Forest Model') %>% 
  kable_styling(full_width = F, position = "left")

Best Random Forest Model

mtry	trees	min_n	.metric	.estimator	mean	n	std_err	.config
8	266	13	rmse	standard	49.63054	10	2.311282	Preprocessor1_Model525

We will now finalize the our workflow with the best model and fit it onto the training set. Below is a graph that shows which predictor variables are the most important to predicting worldscore in the Random Forest model. The higher the value of Importance, the more important it is to the model. We can see that fpavg is the most important variable, followed by spavg, and then if skater is male.

final_flow <- finalize_workflow(rf_flow, rf_best)
final_model <- fit(final_flow, data = score_train)

final_model %>% extract_fit_parsnip() %>% 
  vip() +
  theme_bw()

Testing the model

Now, we will fit the model onto the testing set and see how well the model performs on unseen data. Below is the RMSE value for the model on the testing set. The RMSE value is about 46.6, which is a better value than the RMSE from our training set. So, we can say that our model is performing well on the testing set.

final_test <- augment(final_model, new_data = score_test) 
final_test%>% 
  rmse(truth = worldscore, estimate = .pred)

# A tibble: 1 × 3
  .metric .estimator .estimate
  <chr>   <chr>          <dbl>
1 rmse    standard        46.6

We can also see a plot of the predicted values versus the real values below. The points are relatively close to the line, so our model is performing pretty well. We can also see 2 clusters of points, where the skater did not reach the free skate and has a lower score and where the skater finished both segments for a higher total score. For only short program scores, we can see that the model tends to overestimate them. On the other hand, scores that reflected 2 segments tend to be underestimated.

final_test %>% 
  ggplot(aes(x = .pred, y = worldscore))+
  geom_point()+
  geom_abline()+
  theme_bw()

Something interesting would be to see how the rankings on the competition would change based on the predicted scores. Below is a slopegraph that shows the rankings of the skaters at the 2023 World Championships based on their predicted scores versus their actual rankings from their actual scores.

Below is the women’s change in ranks. The rankings do change quite a bit, but we did get the same skaters in the top 5 rankings.

compare <- augment(final_model, new_data = data)
f_23 <- compare %>% 
  filter(sex == 'Female', year == 2023) %>% 
  select(skater, .pred)
f_23 <- f_23[order(f_23$.pred, decreasing = TRUE),] %>% 
  mutate(predrank = 1:nrow(.))
f23compare <- merge(worldmerged %>% filter(year == 2023), f_23, by = c('skater'))
f23compare <- f23compare%>% 
  mutate(worldrank = ifelse(worldrank > 24, worldrank -5, worldrank)) %>% 
  pivot_longer(cols = c(worldrank, predrank), names_to = 'type', values_to = 'rank')


newggslopegraph(dataframe = f23compare,
                Times = type,
                Measurement = rank,
                Grouping = skater,
                ReverseYAxis = TRUE,
                Title = '2023 Figure Skating Championships - Womens',
                SubTitle = 'Rank Based on Predicted Vs. Actual Scores',
                Caption = '')

Below is the graph for the men’s. The men’s is more accurate as we got 6 rankings correct and also have the same skater’s in the top 5.

m_23 <- compare %>% 
  filter(sex == 'Male', year == 2023) %>% 
  select(skater, .pred)
m_23 <- m_23[order(m_23$.pred, decreasing = TRUE),] %>% 
  mutate(predrank = 1:nrow(.))
m23compare <- merge(worldmerged %>% filter(year == 2023), m_23, by = c('skater'))
m23compare <- m23compare%>% 
  mutate(worldrank = ifelse(worldrank > 4, worldrank - 1 , worldrank))%>%
  mutate(worldrank = ifelse(worldrank > 10, worldrank -1, worldrank)) %>%
  mutate(worldrank = ifelse(worldrank > 18, worldrank -1, worldrank)) %>%
  mutate(worldrank = ifelse(worldrank > 26, worldrank -1, worldrank)) %>%
  pivot_longer(cols = c(worldrank, predrank), names_to = 'type', values_to = 'rank')


newggslopegraph(dataframe = m23compare,
                Times = type,
                Measurement = rank,
                Grouping = skater,
                ReverseYAxis = TRUE,
                Title = '2023 Figure Skating Championships - Mens',
                SubTitle = 'Rank Based on Predicted Vs. Actual Scores',
                Caption = '')

Predicting 2024 Worlds

We can also use our model to predict the scores for the 2024 World Championships. We will use the Random Forest model and use data from the regular competitions throughout the 2023-2024 season to predict the scores at 2024 Worlds. I got the list of skater’s entered from the ISU website and web-scrapped it into an Excel sheet. I did the same process as the other dataset to clean up the data. Since the model does not hold any data for the year of 2024, I put the year for the 2024 data as 2023. Some skaters also did not have any competition data this season, so I filled it in with any scores they had from previous seasons. If they didn’t have any data at all, I just gave them 0’s for scores. Below are the predictions for the Men’s and Women’s events:

#Read in 2024 Entries
entries <- read_xlsx("2024 entires.xlsx", sheet = '2024 Entries')
entries <- entries %>% 
  filter(is.na(Sub)) %>% 
  clean_names() %>% 
  select(skater) %>% 
  mutate(skater = str_to_title(skater)) %>% 
  mutate(year = as.factor(2023))

#Clean up data for scores in 2024
score24 <- clean_names(score24)
score24$skater <- gsub('[0-9]', '', score24$skater)
score24$skater <- str_squish(score24$skater)

#get average scores for 2024
avg2 <- score24 %>% 
  group_by(skater) %>% 
  mutate(spavg = mean(sp_score,na.rm = TRUE)) %>% 
  mutate(fpavg = mean(fs_score, na.rm=TRUE)) %>% 
  select(skater, competition, spavg, fpavg, sex)

#pivot table to match original dataset
avg2$truth <- 1
pivoted2 <- avg2 %>% 
  pivot_wider(names_from = competition, values_from = truth)
pivoted2[is.na(pivoted2)] <- 0

#merge together 
worlds24 <- left_join(entries, pivoted2, by = c('skater'))

#fill in 0 for missing competition variables
worlds24$GPITA <- as.factor(0)
worlds24$GPRUS <- as.factor(0)
worlds24$GPGBR <- as.factor(0)
worlds24$OLY <- as.factor(0)

#factor competition variables
for (i in 6:18) {
  if (is.numeric(worlds24[[i]])) {  # Check if the column is numeric
    worlds24[is.na(worlds24[[i]]), i] <- 0  # Replace NA values with 0
    worlds24[[i]] <- factor(worlds24[[i]])  # Convert column to factor
  }
}

#fill in missing sex variables
worlds24[c(7,21,22),5] <- 'Female'
worlds24[c(36,46, 65,73),5] <- 'Male'

#fill in missing score variables with latest scores or 0
for (i in c(7, 21, 22, 36,46, 65,73)) {
  if (i == 7) {
    worlds24[7, c(3,4)] <- data[93, c(4,5)]
  } else if (i == 46) {
    worlds24[46, c(3,4)] <- data[299, c(4,5)]
  } else if (i == 73) {
    worlds24[73, c(3,4)] <- data[130, c(4,5)]
  } else {
    worlds24[i, c(3,4)] <- 0
  }
}

#predictions
table <- augment(final_model, new_data = worlds24)

#female table
female <- table %>% 
  filter(sex == 'Female') %>% 
  select(skater, .pred) 
female[order(female$.pred, decreasing = TRUE),] %>% 
  mutate(rank = 1:nrow(.)) %>%
  kable(caption = '2024 Predictions - Women') %>% 
  kable_styling(full_width = F, position = "left")%>% 
  scroll_box(width = "100%", height = "200px")

2024 Predictions - Women

skater	.pred	rank
Kaori Sakamoto	218.92409	1
Loena Hendrickx	210.59419	2
Isabeau Levito	208.09762	3
Chaeyeon Kim	192.58412	4
Hana Yoshida	187.45573	5
Nina Pinzarrone	187.36935	6
Livia Kaiser	181.09405	7
Mone Chiba	179.71017	8
Amber Glenn	179.55052	9
Anastasiia Gubanova	177.64526	10
Niina Petrõkina	174.87683	11
Sarina Joos	172.70468	12
Haein Lee	171.98760	13
Kimmy Repond	166.77064	14
Ekaterina Kurakova	166.13705	15
Tzu-Han Ting	163.24541	16
Lorine Schild	163.17047	17
Madeline Schizas	160.49636	18
Julia Sauter	146.85595	19
Josefin Taljegård	143.56948	20
Nataly Langerbaur	142.89156	21
Eliska Brezinova	141.67570	22
Kristina Isaev	139.64798	23
Young You	135.95691	24
Sofja Stepchenko	131.24583	25
Olga Mikutina	128.59074	26
Nina Povey	125.92183	27
Mariia Seniuk	125.64937	28
Alexandra Feigin	122.10273	29
Nella Pelkonen	121.81301	30
Anastasia Gozhva	104.83027	31
Meda Variakojyte	66.13431	32
Anastasia Gracheva	66.13431	33
Mia Caroline Risa Gomez	61.64168	34
Vanesa Selmeková	56.76028	35

#male table
male <- table %>% 
  filter(sex == 'Male') %>% 
  select(skater, .pred) 
male[order(male$.pred, decreasing = TRUE),] %>% 
  mutate(rank = 1:nrow(.)) %>%
  kable(caption = '2024 Predictions - Men') %>% 
  kable_styling(full_width = F, position = "left")%>% 
  scroll_box(width = "100%", height = "200px")

2024 Predictions - Men

skater	.pred	rank
Ilia Malinin	293.94883	1
Yuma Kagiyama	286.89809	2
Shoma Uno	280.90844	3
Adam Siao Him Fa	278.80246	4
Kao Miura	273.11882	5
Jason Brown	253.04533	6
Gabriele Frangipani	252.82965	7
Mikhail Shaidorov	250.89532	8
Aleksandr Selevko	250.82248	9
Nika Egadze	250.55149	10
Lukas Britschgi	249.62953	11
Boyang Jin	244.90687	12
Junhwan Cha	242.84779	13
Georgiy Reshtenko	228.41120	14
Vladimir Litvintsev	227.96176	15
Vladimir Samoilov	226.46857	16
Mark Gorodnitsky	216.60233	17
Roman Sadovsky	213.09935	18
Camden Pulkinen	212.89196	19
Nikolaj Memola	211.51361	20
Adam Hagara	208.12472	21
Wesley Chiu	204.16965	22
Sihyeong Lee	196.53445	23
Nikita Starostin	194.85654	24
Luc Economides	191.14220	25
Andreas Nordebäck	190.70275	26
Deniss Vasiljevs	190.16392	27
Ivan Shmuratko	179.30869	28
Donovan Carrillo	177.36139	29
Tomás Guarino Sabaté	176.09272	30
Maurizio Zandrón	173.39120	31
Aleksandr Vlasenko	128.38718	32
Davidé Lewton Brain	120.96139	33
Valtter Virtanen	94.97995	34
Burak Demirboga	90.69672	35
Edward Appleby	89.69571	36
Jari Kessler	88.47896	37
Alexander Zlatkov	82.61716	38
Semen Daniliants	68.31597	39
Hyungyeom Kim	68.31597	40

Conclusion

After fitting different models onto our training data with cross validation, we found that a Random Forest model was best at predicting the scores of skaters at the World Championships. This makes since typically Random Forests are flexible models that have higher predictive power. However, there is definitely room to improvement. We found that there was overestimation for scores that only came from the short program while underestimation from scores that came from both segments.

The worst performing was the K-Nearest Neighbors model. While it is easy to use and understand, the model does not perform well when there are a lot of variables and is sensitive to outliers, imbalanced data and overfitting. Since we have a lot of predictor variables and our score variables can have an imbalanced distribution, it makes sense that K-Nearest Neighbors did not do too well.

For the future, it would be interesting to do this prediction again but with variables that could describe the physical state of a skater or take into account the previous season’s scores. For example, in our 2024 Women’s predictions, we predicted Young You to be 24th place, but she has been making a comeback after a bad injury and after seeing her most recent domestic competition, I would predict she could be top 10. Since we also had that discrepancy in scores because of the limit of skaters that are allowed in the free skate, it would also be interesting to add a component that showed how many competitors there are or something to weigh how likely they are to get into the free skate. The overestimation for the one-segment scores come from we have data where a skater’s free skate score can be really high. But then suddenly for Worlds, their free skate score is 0. More competitors could also make it harder to make it to the free skate. It would be interesting to find a way to account for this to make the model more accurate in the score predictions.

Wakaba Higuchi - Women’s Figure Skating

Sources

SkatingScores, skatingscores.com/. Accessed 17 Mar. 2024.

“Figure Skating - International Skating Union.” International Skating Union, https://www.isu.org/figure-skating. Accessed 17 Mar. 2024.